Reliability Optimization of Urban Logistics Systems (Uncertainty and Operations Research) 9811906297, 9789811906299

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Uncertainty and Operations Research

Hao Zhang

Reliability Optimization of Urban Logistics Systems

Uncertainty and Operations Research Editor-in-Chief Xiang Li, Beijing University of Chemical Technology, Beijing, China Series Editor Xiaofeng Xu, Economics and Management School, China University of Petroleum, Qingdao, Shandong, China

Decision analysis based on uncertain data is natural in many real-world applications, and sometimes such an analysis is inevitable. In the past years, researchers have proposed many efficient operations research models and methods, which have been widely applied to real-life problems, such as finance, management, manufacturing, supply chain, transportation, among others. This book series aims to provide a global forum for advancing the analysis, understanding, development, and practice of uncertainty theory and operations research for solving economic, engineering, management, and social problems.

More information about this series at https://link.springer.com/bookseries/11709

Hao Zhang

Reliability Optimization of Urban Logistics Systems

Hao Zhang School of E-Commerce and Logistics Beijing Technology and Business University Beijing, China

ISSN 2195-996X ISSN 2195-9978 (electronic) Uncertainty and Operations Research ISBN 978-981-19-0629-9 ISBN 978-981-19-0630-5 (eBook) https://doi.org/10.1007/978-981-19-0630-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

The reliability of logistics system is an important part of logistics system theory. Professor Zhang Hao’s monograph Reliability Optimization Methods for Urban Logistics System presents a thorough study of urban logistics system from the perspective of reliability. It starts with the hot issues in urban logistics and bases itself on the in-depth research on related literature and meticulous analysis of the current situation of urban logistics. By applying the research methods and tools such as matter element analysis and ant colony algorithm and taking into consideration the specific situation of the urban logistics system in China, the author has made several innovative achievements concerning the measure of urban logistics reliability and the exploration and optimization of influencing factors. Furthermore, the author has gone beyond theoretical innovation, promoted the application of theory through the analysis of calculation cases, and made constant improvements in the process of solving practical problems, which endows this monograph with both theoretical value and practical value. It is a monograph of depths on the reliability of urban logistics system. The author, Prof. Zhang Hao, has been engaged in logistics teaching and research for more than ten years. The monograph reveals his academic attainment in the study of urban logistics system. The wording is clear and rigorous, and the content well-arranged and substantial. Given that the research on the reliability of urban logistics system is in its infancy, the monograph has already put forward a theoretical system as a pioneer. It will definitely arrest the attention of more professionals to the research on urban logistics system and its reliability. The book is professional and also readable in terms of content, structure, and logic with proper illustrations. Readers who concern themselves with logistics and supply

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chain will get what they need from this book and pay more attention to the reliability of urban logistics system.

Beijing, China May 2020

Mingke He Vice President, Professor, and Doctoral Supervisor at Beijing Institute of Materials Deputy Director of the Steering Committee for Teaching Logistics Management and Engineering Ministry of Education

Preface

Urban logistics is a process to achieve sustainable urban economic and social development through the flow of all kinds of commodities within the city, especially the transportation of goods for integrated coordination, reasonable planning, and overall control, to solve such logistics problems as traffic congestion, environmental pollution, and energy waste, to reduce the burden on the urban environment and achieve optimal city-wide logistics activities. With the economic development and expansion of the city, customers have brought forward more diversified and personalized requirements for distribution. Frequent transactions have led to the rapid growth of logistics quantity and thus played a role in promoting the development of urban logistics. Meanwhile, more “urban maladies” stood out with increasing contradictions between urban logistics and the city itself. Frequent and unreasonable logistics activities in the city have imposed more pressure on the already heavy traffic and reduced the efficiency of logistics. Reliability research has been quite widely used in several fields but only applied to logistics system in recent years. There is now a serious waste of urban logistics resources. Urban logistics are not so reliable due to all kinds of uncontrollable events and thus fail to meet the growing urban demand—express companies often work at an overload during holidays, for example. The reliability of the logistics system will be greatly reduced or even disabled in the face of unexpected situations, such as serious natural disasters and large-scale emergencies (COVID-19 in 2020 and other public health events). Therefore, the application of reliability theory to the optimization of urban logistics system is of great practical significance to establishing a more reliable urban logistics system in an increasingly complex and turbulent market environment. Improving the reliability of urban logistics system helps enhance the circulation efficiency and ensure circulation safety. With higher reliability, manufacturers may enjoy higher production flexibility and lower out-of-stock costs; trading enterprises may see distribution costs cut down and decision-making support for inventory strategies; urban residents may have goods supplied without delay and their living standards improved; cities may see less urban traffic pressure, a better urban environment, the role of urban logistics as reservoirs brought to full play, their core

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competitiveness improved, and their economy further rationalized. Therefore, the study of reliability optimization for urban logistics system is crucial. The book studies urban logistics system from the perspective of reliability and resorts to the theory of urban logistics and system reliability, with the research on urban logistics system reliability as the thread and matter element analysis and ant colony algorithm as the main tools. It revolves around the meaning, influencing factors, measure, optimization, and other issues of urban logistics system reliability. By analyzing the influencing factors and components of urban logistics system reliability, the measure of reliability is studied, and the reliability optimization model for urban logistics system is established. There are seven chapters. Chapter 1 illustrates the background, theory, and practical significance of the research on urban logistics system reliability, reviews the studies related to urban logistics and system reliability, and analyzes the current status of research. Chapter 2 presents the relevant theories and the concepts related to urban logistics and system reliability, so as to provide a theoretical basis for further research. Chapter 3 describes the meaning of urban logistics system reliability from the two aspects of concept and composition, so as to lay a foundation for withdrawing the factors that affect the reliability of urban logistics system and expounding their mechanism of action. In Chap. 4, the factors influencing the reliability of urban logistics system are analyzed based on the relationship between urban logistics and urban development. The analysis goes deeper, and the withdrawing model is presented. Chapter 5 describes the measure of urban logistics system reliability, establishes the reliability measure model from the perspective of suppliers, distribution centers and customers, and analyzes the degree of influence on the whole system after the failure of each part. In Chap. 6, the reliability optimization model for urban logistics system is studied. First, the reliability of urban logistics system is optimized based on the identification of key sections, and a model based on mobility reliability is established and solution obtained. Then, the reliability allocation model for urban logistics system is proposed based on the generalized cost function. Chapter 7 focuses on case analysis. The cases for the urban logistics reliability allocation model are simulated from the three aspects of influencing factors, reliability measure, and reliability optimization, with the fresh agricultural products e-commerce logistics system as an example. The research on the reliability of urban logistics system is in its infancy. This book plays an introductory role in integrating the reliability theory into the urban logistics system. It is also of a certain practical and theoretical significance to improving the efficiency of urban logistics, promoting the development of urban economy, and enriching the relevant theoretical system. It is hoped that it can further promote research on urban logistics system and attract more scholars to the reliability of urban logistics system. This book has received great support and assistance from scholars such as M. K. He, H. H. Yang, L. Cui, J. J. Lu, J. J. Wang, and J. M. Zhang, during the preparation and publication. They have made valuable and helpful suggestions, for which I would like to express my sincere gratitude. My students, Xu Shensi, Li Hong, Liu Kuo, Zhang Nan, and Zhao Xin, have also made great efforts in literature compilation, modeling, numerical simulation, and proofreading, for whom I feel grateful. Additionally, I

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would like to thank Editor Drizzle Yuan of Economic Science Press for putting a lot of effort into the publication of the monograph. The publication of this book has been funded by the Beijing Philosophy and Social Science Project (Research on the Wisdom of Beijing’s Trade and Distribution Industry and the Deconstruction of Non-Capital Functions, Project No. 17GLB013) and the Beijing “Construction of Highly Accurate Disciplines (Municipal Level)— Business Administration” Project (Project No. 19005902053). I would like to express my sincere gratitude. During the preparation of this book, a large number of relevant documents have been referred to, including the works, reports, and papers by experts and scholars at home and abroad, which are listed in the references at the end of the book as far as possible. There are inevitably omissions, however, for which I want to make an apology. To all the writers, I would like to express my most sincere thanks. This book is the second edition of a revised study of the reliability optimization for urban logistics system (first edition), published in May 2014. This book may contain some errors, and I look forward to corrections by other scholars and experts. I sincerely hope to discuss the issues with people interested in the field of study. Beijing, China October 2019

Hao Zhang

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Background and Significance of the Study . . . . . . . . . . . . . . . . . . . . . 1.1.1 Background of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Significance of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Urban Logistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 System Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Research Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 2 6 11 11 19 21 24

2 Relevant Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Urban Logistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 The Concept of Urban Logistics . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Nature and Characteristics of Urban Logistics . . . . . . . . . . . . 2.1.3 Development Model of Urban Logistics . . . . . . . . . . . . . . . . . 2.2 System Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Reliability Feature Vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 System Reliability and Calculation . . . . . . . . . . . . . . . . . . . . . 2.2.4 System Reliability Failure Analysis . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

29 29 29 32 35 38 38 41 42 46 48

3 Meaning of the Reliability of Urban Logistics System . . . . . . . . . . . . . . 3.1 The Concept of Reliability of Urban Logistics System . . . . . . . . . . . 3.1.1 Urban Logistics System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Reliability of Urban Logistics System . . . . . . . . . . . . . . . . . . . 3.2 Components of the Reliability of Urban Logistics System . . . . . . . . 3.2.1 Node Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Route Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Network Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51 51 51 55 58 58 68 70 77

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4 Factors Influencing the Reliability of Urban Logistics System . . . . . . . 4.1 The Relationship Between Urban Logistics and Urban Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Urban Logistics and Commerce . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Urban Logistics and Industry . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3 Urban Logistics and Urban Transportation . . . . . . . . . . . . . . . 4.1.4 Urban Logistics and People’s Livelihood . . . . . . . . . . . . . . . . 4.1.5 Urban Logistics and Urban Environment . . . . . . . . . . . . . . . . 4.2 Analysis of Influencing Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Operational Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Reliability of Technical Equipment . . . . . . . . . . . . . . . . . . . . . 4.2.4 Policies and Regulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Force Majeure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Refined Model of Influencing Factors . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Matter Element Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Gray Correlation Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Weight Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79 79 82 83 84 86 88 91 92 99 102 106 111 114 116 118 120 121

5 Measurement of Reliability of Urban Logistics System . . . . . . . . . . . . . 5.1 Reliability Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Reliability Measure Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Reliability of Supplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Reliability of Distribution Center and Customer . . . . . . . . . . 5.2.3 Reliability of the Distribution Network Model . . . . . . . . . . . . 5.3 Influence Degree Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Method of Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Selection Indicators and Principles . . . . . . . . . . . . . . . . . . . . . 5.4.2 Index Standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Critical Effect Treatment of Reliability in Urban Logistics System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Determination of Index Weight . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.5 Measurement of System Reliability . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Study of the Reliability Optimization Model for the Urban Logistics System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Logistics System Reliability Simulation . . . . . . . . . . . . . . . . . . . . . . . 6.2 Identification of the Critical Section . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Logistics System Optimization Based on Mobility Reliability . . . . . 6.3.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Basic Assumptions and Symbol Descriptions . . . . . . . . . . . . 6.3.3 Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Model Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6.4 Reliability Allocation Model for Urban Logistics System . . . . . . . . . 6.4.1 Reliability Allocation Method . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Reliability Allocation Principle . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Construction of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Case Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Withdrawal of the Influencing Factors . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Build the Matter-Element Matrix . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Build the Matter-Element Evaluation Model . . . . . . . . . . . . . 7.2 Reliability Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Failure Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Degree of Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Reliability Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Identification of Key Sections . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Optimization Based on Unimpeded Reliability . . . . . . . . . . . 7.4 Reliability Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Analysis on Influencing Factors of Reliability of e-Commerce Logistics System for Fresh Agricultural Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Failure Model of e-Commerce Logistics of Fresh Agricultural Commodities Based on Bayesian Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.3 Simulation of e-commerce Logistics Failure Model of Fresh Agricultural Products . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.4 Parameter Estimation and Test of Reliability Allocation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.5 Example Analysis of Reliability Allocation Model . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 1

Introduction

City, the distribution center of commodities, plays the role of the “growth pole” in economic development. It has promoted the development of the whole regional economy, and seen great demand for logistics. Urban logistics, like blood vessels in the human body, run through the whole urban system. It is a logistics service support system to meet the needs for urban production and life and ensure the operation of the city. It also reflects the competitiveness of a city. Urban logistics is a regional logistics activity carried out in a city. Its development degree should match that of the city on the whole, and its role in urban economic, social and environmental development should support the sustainable development of the city. With well-equipped and advanced infrastructure, high operation efficiency, high energy utilization rate, and environmental friendliness, urban logistics can not only support urban development and resident needs for logistics in the daily life, but also promote the sustainable development of urban economy, society and environment. In contrast, urban logistics development and operation with low efficiency and at the expense of the environment will inevitably limit the comprehensive development of the city [1]. With the acceleration of urbanization, the expansion of urban scale and the improvement of population density, cities have an increasing demand for logistics. Cities see an urgent need to establish an efficient and standardized urban logistics system to meet their need for development and the needs of people. Due to the traffic congestion, however, the contradiction between the huge logistics demand and the limited road traffic resources has stood out, and the problems regarding reliability of urban logistics emerged.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. Zhang, Reliability Optimization of Urban Logistics Systems, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-19-0630-5_1

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1.1 Background and Significance of the Study 1.1.1 Background of the Study Logistics is the “third source of profit” and one of the “top ten sunrise industries”. Government departments in China have attached great importance to the logistics industry and provided greater policy support to it by issuing a series of policies and measures. For example, the Ministry of Commerce issued the Guidance on Accelerating the Development of Modern Logistics in China’s Circulation Sector (2008), the State Council issued the Logistics Industry Adjustment and Revitalization Plan (2009), the General Office of the State Council issued the Opinions on Policies and Measures to Promote the Sound Development of the Logistics Industry (2011), and the Opinions on Deepening the Reform of the Circulation System to Accelerate the Development of the Circulation Industry (2012), so as to effectively alleviate the tax burden on logistics enterprises and increase land policy support for the logistics industry. The Medium and Long-term Plan for the Development of the Logistics Industry (2014–2020) issued by the State Council (2014), the Special Action Plan for Reducing Costs and Increasing Efficiency of the Logistics Industry (2016–2018) issued by the National Development and Reform Commission (2016), the ThreeYear Action Plan for Promoting transportation Structure Adjustment (2018–2020) issued by the General Office of the State Council (2018), the National Logistics Hub Layout and Construction Plan (2018) jointly issued by the National Development and Reform Commission and the Ministry of transportation have effectively and efficiently helped reduce the continuing high costs for China’s logistics industry and enabled it to develop better and faster. Seven commissions and departments including the Ministry of transportation jointly issued the Opinions on Strengthening and Improving the Management of Urban Distribution (2013), which put forward opinions on improving the management system and mechanism, enhancing infrastructure support capacity, strengthening transportation market management, optimizing access control measures, increasing law enforcement supervision, and accelerating the promotion and application of science and technology, so as to create a proper institutional mechanism and policy environment for the sound and orderly development of urban distribution. The formulation and improvement of these policies and regulations promote not only the steady and rapid development of the logistics industry itself and industrial adjustment and upgrading, but also the adjustment and development of services and other supporting industries, which is of great significance to promoting industrial restructuring, transforming the mode of economic development and enhancing the competitiveness of the national economy. During the national economic operation, the logistics industry both consumes the products and services of other industries and provides products and services for associated industries. The modern logistics industry is an emerging, compound type that spans various other industries and sectors and covers enterprises and government departments engaged in logistics related economic activities. It features many types of operation. As an important part of modern circulation, the logistics industry is closely related to the

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development of national economy. In particular, the sound development of urban logistics is the basis for ensuring the smooth operation of various industries and an important part to accelerate urban economic development and improve circulation efficiency [2]. The relevance of the logistics industry in Beijing is analyzed below, for example. Figure 1.1 shows that there are extensive and close techno-economic links between the logistics industry and others in economic activities. The logistics industry in Beijing is one with “low added value and a high driving force” as it is a great driving force behind upstream industries. In the intermediate link of the economic production in Beijing, the logistics industry accounts for a large proportion and plays an important role in supporting the production process of other economic sectors. It can be seen from the analysis that the logistics industry, communication equipment, computer and other electronic equipment manufacturing sectors, wholesale and retail industry, chemical industry, petroleum processing, coking and nuclear fuel processing industry, transportation equipment manufacturing industry, financial industry and others are closely related to the logistics industry. Therefore, the reliability of urban logistics directly affects the orderly and smooth development of related industries. In Beijing, for example, as shown in Fig. 1.2, the regional GDP nearly tripled from 2008 to 2017, and reached 2801.49 billion yuan in 2017. The rapid economic growth provided vast space for the development of modern logistics in Beijing. In terms of its industrial structure, the three industries show a 3-2-1 pattern, with the tertiary industry as the pillar. The three industries in 2017 accounted for approximately 0.43, 19.01 and 80.5%. Beijing is a comprehensive industrial city as well as a tourist city with high living standards, for which huge logistics demand is seen in Beijing. Therefore, it is urgent to establish a reliable and flexible urban logistics system to satisfy its demand in this regard. The expansion and economic growth of the city promote the prosperity of its commodity market. The demand for urban logistics is also increasing, and customer

Fig. 1.1 Complete consumption coefficients for logistics and selected industries. Source Wu Haijian [2]

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Fig. 1.2 Changes in the economic and industrial structure of Beijing. Source Beijing Municipal Bureau of Statistics [3]

demands more diversified and random, which pose a greater challenge to logistics and delivery. Customers have higher requirements for the timeliness and accuracy of distribution. Personalized modes of ordering require rapid response of distribution service. Therefore, reasonable planning for the urban logistics system is critical. An efficient urban logistics system can meet the needs of people for material life, save social costs, and improve the quality of life of residents. Figure 1.3 shows the total annual volume of urban freight transportation in municipalities directly

Fig. 1.3 Freight volumes with major modes of transportation in municipalities directly under the central government in China. Source Beijing Municipal Bureau of Statistics [3]. Shanghai Municipal Bureau of Statistics [5]. Tianjin Municipal Bureau of Statistics [6]. Chongqing Municipal Bureau of Statistics [7]

1.1 Background and Significance of the Study

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under the central government in China in the past eight years. However, the current development of urban logistics cannot meet the growing demand for urban logistics, and various events hardly under control lead to lack of guarantees for the reliability of urban logistics system, which thus fails to keep up with the rapidly developing urban economy and society. For example, the huge sales volume of the “Double Eleven” e-commerce event in 2012 caused overload and delay for express companies. The online shopping promotion brought an unprecedented high sales volume to ecommerce companies, and also delivery problems. Statistics from the official website of Yuantong Express show [4] that the number of orders received on the night of November 11, 2018, exceeded 100 million again, 6 h and 53 min earlier than in 2017. Statistics from the State Post Bureau show that during the “Double Eleven” period in 2018 (November 11–16), express enterprises across China handled a total of 1.881 billion mail (express) packages, an increase of 25.8% year-on-year. The peak, 416 million packages, was seen on November 11, an increase of 25.68% over last year and 3.2 times that on other days. It hit a new record with the highest daily handling volume for express companies in China. However, with the further acceleration of China’s urbanization process, urban transportation layout and urban infrastructure construction considerations, along with the negative effects from urban development, have restricted the development of urban logistics in terms of either urban economic development or urban spatial structure. With the continuous expansion of the urban scale and the rapid increase in the urban population, industrial transfer shows an increasingly obvious trend. Also with the even serious “urban maladies”, such as traffic congestion, resource scarcity and the decline in the living quality of urban residents, the progress of cities has been hindered. The continuous investment in urban transportation construction has greatly increased the carrying capacity of urban transportation infrastructure and also greatly supported the socioeconomic development of cities. However, with the continuous increase in the urban population and the rapid increase in motor vehicle ownership, there is a large gap between the transportation business and the growing urban demand. Problems with urban transportation are shown in large quantities and cause the failure of meeting the growing demand. The report of the 19th National Congress points out that China’s urbanization rate has increased by an average of 1.2% points annually in the past five years, and more than 80 million people have transferred from agricultural to urban residents [8]. Data from China Statistical Yearbook 2018 show that China’s urbanization rate had reached 58.52% as of 2017 [9]. The Blue Book on Investment: The China Investment Development Report (2013) shows that by 2030, China’s urbanization rate will reach 70%, and the population living in urban areas will exceed 1 billion, and that the rate of urban road infrastructure construction is out of sync with the rate of urbanization will inevitably result in greater traffic pressure [10]. As of 2017, there were 5.64 million motor vehicles in Beijing, which ranked first in China in this regard, and the problem of urban traffic congestion in Beijing is becoming increasingly serious. According to the 2012 AQTI Evaluation Report, the serious air pollution in some areas of China mainly comes from coal combustion, traffic and dust. The report

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shows that in nine major cities including Beijing, the 24 h average concentrations of nitrogen dioxide and PM10 are characterized by significant “double peaks”, which occur at approximately 9 a.m. and 21 p.m., mainly due to emissions from traffic sources and traffic dust. The Ministry of Environmental Protection announced that approximately one-fifth of China’s cities suffered serious air pollution, and more than one-third of the 113 key cities had air quality that failed to meet the national level-2 standard, and exhaust emissions from motor vehicles constituted the main source of urban air pollution. The above data show that urban air pollution comes partly from vehicle exhaust, and urban logistics distribution vehicles account for a large part of motor vehicles in the cities, so that the planning for urban logistics system can help optimize the configuration of vehicles and reduce polluting vehicle exhaust. Severe natural disasters and sudden large-scale accidents may also lead to reduced reliability or even gridlock of the logistics system, thus seriously affecting the economic development and livelihood of citizens. For example, due to the nuclear radiation crisis in Japan in 2011, there was a rush for salt in many cities such as Guangdong, Zhejiang, Jiangsu, Fujian, Shanghai and Chongqing. Despite sufficient salt stockpiles, the supply of salt in the emergency failed to meet the demand in the market, leading to panic among residents and negative impacts. Urban economy and urban logistics are complementary, as the former is the condition for the latter, and the latter an integral part of the former. Cities are the distribution centers for commodities, and urban logistics the pillar that supports the normal functioning of cities. The production and living materials required for urban development need the support of an efficient and reasonable logistics system. An efficient logistics system in cities can help enterprises shorten the circulation duration of commodities, reduce logistics costs, and focus on their main business. For society as a whole, it can optimize resource allocation and reduce total social costs. Logistics, as an indispensable part in the economic chain to reduce transaction costs and improve regional competitiveness, have become an important support for the urbanization process. A scientific and reasonable urban logistics system can improve the risk resistance of urban logistics in case of unexpected events, promote the development of industrial clusters, facilitate the smooth and efficient development of urban economy, and better meet the needs for production and living materials. Therefore, how to build a reliable urban logistics system in the complex and changeable urban system is an urgent problem to be solved.

1.1.2 Significance of the Study As the center of economic growth, cities are facing more serious challenges concerning the development of urban logistics due to the restrictions in population, transportation environment and policy. In the context of economic globalization, the study of the reliability of urban logistics system helps ensure the needs for urban means of production and living, allocate resources more reasonably, and reduce social costs. First, the book analyzes and sorts out the factors influencing the reliability

1.1 Background and Significance of the Study

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of urban logistics system, clarifies the influencing mode and action mechanism of these factors, puts forward the multidisciplinary collaborative optimization design model for urban logistics system reliability, and finally devises the optimization ideas and methods to effectively reflect the rule for urban logistics system reliability, With the urban logistics and distribution system for fresh agricultural products on e-commerce platforms as an example, the book follows the above ideas, and analyzes the e-commerce and logistics characteristics, based on which to withdraw the factors influencing the logistics for fresh agricultural products on e-commerce platforms and identify the key influencing factors by constructing a Bayesian network model. Further on that basis, it considers the reliability complexity and cost of a unit to build a system reliability allocation model, so as to optimize the system reliability and put forward corresponding suggestions on improvement. The research on the reliability of urban logistics system is still in its infancy currently. The reliability related composition, characteristics, judgment, evaluation and control will be the research focus in the future. Therefore, the research on the reliability of urban logistics system is of great practical and theoretical significance.

1.1.2.1 (1)

Practical Significance

It is conducive to the smooth and rapid socioeconomic development of the city. The logistics industry is an important indicator of the prosperity of local economy and a basic indicator of a city’s regional status and influencing capacity, as well as one of the comprehensive and pillar industries of national economy with an important role in promoting the development of the city’s economy. The logistics industry contains great potential for development, and cities are the cornerstone for cultivating such a high-growth industry. The reliability of the urban logistics system directly affects the smooth operation of the urban economy. For example, given the development of e-commerce in recent years, if a company in this industry launches promotion and provides discounts, a sudden surge in consumer demand will occur, and the express companies will work at an overload. Due to the gap between the development of the urban logistics system and the growth of urban logistics demand, the economic chain will not run properly. Similarly, in the case of the logistics and distribution system for fresh agricultural products on the e-commerce platform in the city above mentioned, the fresh agricultural products related e-commerce business with a high gross profit rate, great potential for online consumption, and a high repurchase rate is recognized as the “blue ocean” in the e-commerce field. The fresh agricultural products, however, are perishable and thus impose higher requirements for the reliability of the existing urban logistics system. Therefore, to fortify the research on the reliability of urban logistics system and build an optimization mechanism for it with reliability at the core are beneficial to maintaining the stability of social and economic development within the current context of rapid economic development.

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The modern logistics industry has a relatively complete system of nodes and route networks, which can reduce transaction costs in the process of economic activities and improve the efficiency and level of economic activities. The development of the modern logistics industry helps to reduce transaction costs throughout the supply chain. (2)

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It helps to protect the living quality of residents. In the report of the 18th National Congress of the Communist Party of China, it is clearly stated that “In strengthening social development, we must give high priority to ensuring and improving the people’s wellbeing. To improve people’s material and cultural lives is the fundamental purpose of reform and opening up and socialist modernization. We should bring as much benefit as possible to the people, resolve as many difficulties as possible for them, and solve the most pressing and real problems of the greatest concern to them” [11]. It is also stated in the Decision of the CPC Central Committee on Some Major Issues Concerning Comprehensively Deepening Reform, adopted at the Third Plenary Session of the 18th CPC Central Committee, that “Social reform must evolve around the protection and improvement of the people’s livelihood and advance fairness and justice for common prosperity”. It is remarked in the report at 19th CPC National Congress that “Ensuring and improving living standards through development. The wellbeing of the people is the fundamental goal of development. We must do more to improve the lives and address the concerns of the people, and use development to strengthen areas of weakness and promote social fairness and justice” [8]. The distribution in urban logistics is a livelihood project related to social stability, and marks the supply of important materials closely related to urban residents, such as “rice bags”, “vegetable baskets”, and “agricultural products-supermarket partnering”. Strategic reserves of essential goods all require a reliable and effective urban logistics system as a guarantee, without which, people’s basic lives will be affected and a huge impact made on social security and stability, which in turn will cause losses on a more extensive scale. Collaborative optimization of the reliability of China’s urban logistics system can effectively reduce users’ logistics costs, cut down social labor and energy consumption in logistics activities, and ensure product supply and price stability. It is conducive to improving the level of urban management and enhancing the competitiveness of the city. Urban logistics is an important component of the core competitiveness of a city’s regional economy and a key indicator of its level of economic development. The provision of efficient and reliable logistics services, a basic performance requirement and an important part of building a modern city, shows the level of city management. Generally, the core competitiveness of a city is mainly reflected as the joint effect of productivity and circulation capacity. With productivity relatively stable, circulation capacity has become an important influencing factor to determine the competitiveness of a city. Low circulation capacity will not only prevent the effective realization of local productivity, but also cause low circulation through external

1.1 Background and Significance of the Study

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channels, and seriously weaken the competitiveness of the city. The study of the reliability optimization for urban logistics system can help make up for the lack of local productivity and enhance the influence of cities through internal and external exchanges and complementary advantages, so as to strengthen the comprehensive competitiveness of cities. It provides an effective basis for the relevant functional management departments of the city for making decisions. In recent years, the Chinese government has attached great importance to the development of the logistics industry and has introduced a series of policies and regulations to build a platform for the sound development of the logistics industry. Scientific logistics policies exert a positive role in removing the external uneconomical nature of logistics, such as urban traffic congestion and environmental pollution. Well-established policies are also of great significance to promoting the sound and orderly development of the logistics industry and improving the efficiency of urban logistics. Some of China’s existing policies and regulations to a certain extent have supported the development of the logistics industry, but some others with deficiencies in practice and specific implementation have rather become a bottleneck to the development of the modern logistics industry. There is also a lack of systemic, time-sensitive, operable legal standards for the development of modern logistics, and the “policy vacuum” problems. Modern logistics is an interdepartmental, cross-regional and cross-industry compound type involving all aspects of urban management, such as transportation, industrial layout, e-commerce, and commodity circulation. Its reliability management is associated with the functions of several government departments, and will provide an objective and scientific decision-making basis for relevant departments to formulate and improve urban logistics management and control policies.

1.1.2.2 (1)

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Theoretical Significance

It has enriched the theoretical system of logistics management. An urban logistics system is a multidimensional complex system, and the degree of its reliability has a significant impact on the smooth operation of urban economy and society. The research on reliability optimization models and methods for urban logistics system has attracted attention from an increasing number of researchers in the field of logistics management. People are still exploring about the reliability optimization of urban logistics system, and a proper theoretical system of urban logistics system reliability is absent. From the existing research results, there are few studies about the reliability of urban logistics system. An urban logistics system has typical characteristics that distinguish itself from other logistics systems and is directly related to people’s livelihood and social stability. Moreover, its reliability composition, operation, evolution and measure have not been covered by theoretical research, and research in this field more valued. Most of the developed countries and regions in the world, such as the United States, Japan and Europe, have established a well-equipped

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1 Introduction

and scientific urban logistics system after years of exploration and development. China is still in the initial stage in this regard. Based on the theory of urban logistics and system reliability, this study analyzes the meaning and influencing factors of urban logistics system reliability, establishes the measure model for urban logistics system reliability, and estimates and optimizes the reliability of urban logistics system with respect to nodes and routes, so as to build a complete theoretical system in this field. Meanwhile, with the logistics system for fresh agricultural products on the e-commerce platform in cities as an example, this book proposes a Bayesian network based logistics failure model and a reliability allocation model, so as to enrich the theoretical system for e-commerce business in fresh agricultural products. With the features of fresh agricultural products and e-commerce taken into consideration, the logistics characteristics, modes and problems of e-commerce business in fresh agricultural products are clarified, which helps to reduce the rate of loss and spoilage of fresh agricultural products during circulation and promote the better development of e-commerce companies in fresh agricultural products. An innovative research approach and methodology for urban logistics reliability research is proposed. For the study of urban logistics system reliability, there is no effective scientific method to describe and analyze the inherent operation mechanism and evolution law based on existing results. According to thorough analysis and integration of logistics management theory, system reliability theory, system science, complexity science and existing research results, the book redefines the meaning of urban logistics system reliability, highlights that its reliability reflects not only the ability to achieve the specified functions but also the synergy between urban logistics and urban socio-economic development. Based on the analysis of the factors affecting the reliability of urban logistics, the key factors are extracted, and their mechanism of action analyzed with the use of methods such as matter-element analysis and gray correlation calculation. The reliability-based urban logistics network system has been a popular research topic in recent years. The book has built a typical model in combination with hot topics in reality, considered the influence of failure and uncertain demand during the optimization of reliability of nodes and routes, and established reliability-based node planning and mobility based route optimization, so as to provide a new solution to future research in this field. It reveals the inherent scientific laws. The book concludes the key factors affecting the reliability of urban logistics system from five aspects: information, operational capacity, technical equipment reliability, policies and regulations, and force majeure, and analyzes their mechanisms of action. The relationship between urban logistics and the urban economy is analyzed to reveal the role of urban development in safeguarding and promoting logistics and the impact of urban logistics development on urban commerce, transportation and environment. The reliability optimization and simulation of urban logistics system help effectively reveal the law for the evolution of urban logistics system reliability, estimate the reliability of urban logistics system, simulate the reliability

1.1 Background and Significance of the Study

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of urban logistics system, resort to simulation for research, and demonstrate the scientific law in an intelligent, fine and visualized manner. In summary, against the background of urbanization in China, the development of the logistics industry has become an important support for the urbanization process. The study of the reliability optimization of urban logistics system is one of the most popular topics in the field of logistics management and just in the stage of exploration. Considering the many factors and complexity involved in the urban logistics system, the book aims to build an optimized urban logistics system with reliability at the core, formulate optimization strategies, construct optimization models and design reliability optimization methods for urban logistics system by means of simulation. At the theoretical level, it provides a more effective research perspective and path to studying the reliability of urban logistics system, achieves innovation in reliability optimization strategies, models and methods, and helps to enrich and improve logistics management theories. At the practical level, it helps to facilitate the balanced development of urban economy, allocate urban resources rationally, protect people’s livelihood, alleviate economic and social pressure such as that in traffic and environment arising from urbanization, and provide a basis for relevant departments to formulate regulations and control policies.

1.2 Literature Review 1.2.1 Urban Logistics Foreign scholars started early with the study of urban logistics. They have long noted the role of urban logistics in advancing economic sustainability of the city, and proposed the concepts of urban logistics. The concept of urban logistics was formally proposed by Professor Eiichi Taniguchi, a Japanese scholar, in 1999—“City logistics is the process for optimizing the logistics, transportation efficiency and logistics costs by companies considering the traffic infrastructure, real-time road transportation, transportation costs and waste of resources in a city within the framework of a market economy.” With the continuous development of urban economy, the importance of urban logistics has become increasingly prominent, and many scholars at home and abroad carried out in-depth research [12]. Japan is among the first countries to study urban logistics. After Eiichi Taniguchi proposed the concept of city logistics, E. Taniguchi, R. G. Thompson and T. Yamada (1999) from Japan made extensions of it by proposing that city logistics is the process for totally optimizing the logistics and transportation activities by private companies [13]. Foreign scholars believe that the development of modern urban logistics can mitigate the negative effects caused by urban transportation, such as those on urban traffic, urban environment, and lives of urban residents [14], and start the research on urban logistics towards that goal.

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1 Introduction

The research is conducted to reduce the adverse effects of urban logistics activities. The development of urban logistics provides an effective assurance for the development of urban industry and commerce, and facilitates the life of urban residents. However, it has also caused negative impacts, such as traffic congestion and environmental pollution in the city. If the external uneconomical nature cannot be removed, it will hinder the development of the city and also seriously affect people’s livelihood. For that reason, scholars conducted studies aiming at weakening the negative impacts of urban logistics. Eduardo and Romero [15] proposed a multi-factor evaluation model to evaluate the government’s performance in dealing with urban freight transportation problems by quantifying the efforts of the government’s handling of urban freight transportation externally with the urban freight transportation index (UFTI). Xu Wenrui [16] analyzed the idea of analyzing and evaluating urban logistics system in a life-cycle approach and concluded that joint distribution and thirdparty distribution service models can help businesses achieve scale operation and reduce the environmental sacrifice per unit at once, which to some extent indicates the future direction of urban logistics and distribution. The research is conducted to improve the operation efficiency of urban logistics. The efficiency of urban logistics reflects the speed and effectiveness to which the production and living needs of the city are met and directly affects the level of the city’s logistics system. Some scholars proposed specific solutions by optimizing urban transportation and distribution. Xu [17] proposed a logistics resource allocation method based on a dynamic customer base. The calculation results showed that the model can solve the problems regarding customer demand that changes over time and the dynamic management of logistics resources, so that quick response can be made to the urban logistics and distribution demand and the allocation of logistics resources optimized. Woudsma et al. [18] used a spatial autoregressive model to study the relationship between the performance of the transportation system and land use. The result showed that the lag in the land for urban logistics caused transportation congestion, and thus affected the accessibility of the transportation network. Sathaye et al. [19] considered that the atmosphere is more stable at night than during the day, and explored how to improve the efficiency of logistics and transportation at night without increasing environmental pollution. Fan Yuejiao [20] studied the average efficiency of the logistics industry in cities in China as national-level circulation nodes using a panel data model based on the Cobb–Douglas production function with stochastic frontier analysis that takes into account the technological progress factors. The result showed that the average efficiency is most constrained by the level of information technology and industrial structure and that the average efficiency of the logistics industry varies widely in temporal and spatial dimensions from city to city. The research related to urban logistics planning and design is conducted. Some scholars studied the planning for urban logistics, such as the location of urban logistics and distribution centers and the path design for distribution vehicles, which have facilitated the optimal allocation of resources and improved

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the operational efficiency of urban logistics. L. K. Nozick, L. E. Zegolis and others analyzed and studied urban logistics planning and the spatial layout of urban distribution centers. Laura Meade, E. R. Stock, Jukkakopela, and other scholars studied the strategic management of urban logistics using various methods, such as network analysis. Yurimot [21] identified the optimal number and location of public logistics facilities in cities from the perspective of the social transportation system in terms of environment and business efficiency. His research result has been applied in the metropolitan area of Tokyo. Research on logistics at the city level has long been carried out in Europe and the United States. Trevor Hale and Christopher R. Moberg [22] focused on the selection of supply nodes for emergency logistics and, in particular, developed a quantitative model for the amount of emergency supplies stored at the nodes. Considering the dynamic flow of urban traffic, Barcelo et al. [23] established a dynamic traffic simulation model to optimize resource allocation by collecting dynamic data such as the current position and speed of vehicles and outputting their travel paths. Tamagawa et al. [24] used a learning model to solve the vehicle path problem based on time window prediction, considering the behavior of urban freight traffic stakeholders. The result showed that traffic control and charge of highway tolls had helped improve the environment. Awasthi Anjali, Chauhan Satyaveer S., et al. [25] used affinity diagrams to identify the criteria for evaluating urban logistics planning, including technical, social, economic and environmental ones. It allows stakeholders to rate the planning, evaluate the effectiveness of urban logistics planning options with hierarchical analysis, and find the optimal solution among feasible ones [25]. Ehmke et al. [26] provided a time window-based vehicle path planning system for urban logistics service providers, and thus a decision-making basis for vehicle path selection. Zhao Kun, Bai Yi, and Shi Xiaoxia [27] optimized urban logistics and distribution paths and minimized their costs through fuel consumption management with VRPTW modeling based on the fact of traffic control in numerous cities such as truck restrictions, time of restricted access to the central area, and restricted zones. Linert [28] studied about the logistics decision support system for disaster relief and the multi-stage and multi-objective distribution of disaster relief materials. Jesus et al. [29] studied the ways in which governments construct an urban logistics system. Alex J. [30] used the decision tree to find the optimal number of logistics service providers in case of distribution failure and to consider the modeling and solution-finding in the scenario of equal and unequal probability of failure of each supplier. Mitsuo (2008) studied the modeling of a network of urban logistics system. Toshinori Nemoto, Johan Visser, Ryuichi Yoshimoto and others explored the impact of information and communication technologies, with their significant reduction, on urban logistics system. Erdal Kayacan, Baris Ulutas, and Okyay Kaynak [31] proposed that a GM (1, 1) model can achieve desirable forecasting results with little or missing data. Jin Tongdan and Tian Yu [32] examined reliability design and

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1 Introduction

inventory levels, compared various goods, and developed an optimization model to formulate inventory control policies in multiple stages. Because of the earlier development of urban logistics abroad and the completeness of market mechanism, scholars abroad have limited meso results of research on urban logistics, and few have studied the relationship between logistics industry and urban economy from the perspective of urban economic development and industrial structure. At present, most scholars focus their research on the logistics system at the planning level. In recent years, with the development of cities, research on urban logistics has become a hot topic. Although domestic research on urban logistics started late, scholars have made certain achievements in this regard. (1)

Theories of urban logistics. Fang Hong [33], an early researcher on urban logistics in China, conducted an in-depth study of the urban logistics industry, discussed the importance of the integration and optimal management of urban logistics resources, and clarified that the urban attractiveness and competitiveness can only be improved as the development of the urban logistics industry relies on the geographical advantage and unique image of the city. Ding Minglei and Liu Binglian [34] studied the relationship and mechanism of action between the urban logistics system and the economic growth of the city. Lei Kai [35] analyzed the intrinsic relationship between logistics and economic development. The empirical analysis was conducted quantitatively and qualitatively in terms of contribution of the logistics industry to economic growth, economic structure optimization, and economic quality improvement. The evolutionary trend of the role of the logistics industry in economic development was clarified [35]. Liu Binglian and Chen Weibo [36] analyzed the problems and root causes of urban logistics in China, constructed a theoretical framework of urban logistics in terms of both external motives and internal composition, and looked forward to the future development trend of urban logistics. Based on the analysis of the role of urban logistics, Wang Ming [37] proposed a basic strategy for the development of the urban logistics industry in China, namely, to build regional manufacturing centers and distribution centers for the development of regional industry and commerce, and to achieve efficient and accurate urban distribution to meet the needs of cities themselves. Zhu Changzheng [38] analyzed the main problems faced in the development of China’s urban logistics from four aspects, including management concept, urban logistics infrastructure, urban logistics distribution technology and information level, and put forward countermeasures and suggestions to promote the development of China’s urban logistics in response to these problems. Long Hua and Hai Feng [39] studied the positive role of the logistics industry on China’s urbanization process and elaborated its inherent logic. From the perspective of sustainability, Guan Shuicheng and Shen Guicheng [40] explored the important factors influencing the coordinated development of urban economy and logistics, concluded that the coordinated development largely depends on its own

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development level, and obtained two causal chains that affect the coordinated development. Forecasting of urban logistics demand is the basis and premise for the systematic planning for urban logistics. Only by forecasting the future development trend can effective guide be provided on the planning and design of the logistics system. Domestic scholars have done much research on the logistics prediction model, and achieved certain results both theoretically and practically. Zhou Yanhui [41] argued that neural networks are accurate in nonlinear prediction, and thus resorted to a combination of qualitative and quantitative analysis to construct an intelligent model for neural networks, and used the BP algorithm to obtain the future amount of logistics for the park. Luo Shiguang, Ye Sai, Hu Rong et al. [42] held that the adaptive iterative support vector machine method can be adopted for logistics prediction, and selected different modeling approaches to checking and comparing. The results showed that it is feasible and effective to predict the logistics quantity with multioutput support vector machine method [42]. Ruan Qingfang, Miao Lixin, et al. [43] combined the genetic algorithm and BP neural network algorithm to predict the quantity of demand in urban logistics system, which improved the breadth and credibility of logistics demand prediction. Deng Jingchun and Yang Mei [44] combined the characteristics of the four-stage method, proposed the idea of applying the fourstage method in logistics demand forecasting, and analyzed the forecasting methods and models for each stage of logistics demand forecasting. It has better solved the problem concerning prediction of urban logistics, and provided a basis for layout and planning for urban logistics infrastructure [44]. Wen Peina [45] analyzed the socioeconomic factors affecting freight volume and created a BP neural network forecasting model for urban logistics demand with Beijing as an example. Research methods in logistics demand forecasting by Chinese scholars include the gray forecasting method, neural network forecasting method, Holt-winter forecasting method, exponential smoothing forecasting method, and so on. Li Jie, Chen Yanru and Yang Lu [46] created a two-stage combined forecasting model GSPS-BPNN based on the support vector, genetic algorithm, particle swarm algorithm, and BP neural network, and confirmed that the model outperformed the single-stage single forecasting model in accuracy and stability. Wang Xiaoping and Yan Fei [47] established the agricultural cold chain logistics demand forecasting model based on the gray model, support vector machine, BP neural network, RBF neural network, and genetic neural network, evaluated the model in terms of its ability to describe the correlation between variables and its forecasting accuracy, and thus proved that the genetic neural network works better for agricultural cold chain logistics demand analysis. Cai Wanzhen and Huang Han [48] developed a combined forecasting model based on the BP-RBF neural network, and demonstrated higher forecasting accuracy and significantly reduced probability of large errors compared to the single forecasting model. (2)

The research on the evaluation of the operational efficiency of urban logistics is conducted. The operational efficiency of urban logistics directly reflects the level of urban logistics, and affects the economic development and people’s living quality in the city. Some scholars have conducted in-depth studies of

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1 Introduction

the efficiency of urban logistics. Tong Mengda [49] constructed an evaluation indicator system for modern logistics development from the four aspects of demand for modern logistics, service supply, development environment and development effectiveness. Shi Xiuping et al. [50] considered the entirety of the urban logistics system and established the evaluation indicator system from urban logistics activities and the impact of urban logistics. Lv Pu et al. [51] combined the characteristics of urban logistics, argued that the study of urban logistics should consider its relationship with the economic development of the affected area, and established an evaluation indicator system composed of logistics development capacity, energy consumption and environment, and the environment of logistics development. Wang Ana [52] established an evaluation indicator system incorporating logistics development capacity, development environment and impact, and used it as a basis to study the logistics development level of Dalian vertically and horizontally. Wang Mei and Lan Hongjie [1] proposed to use the indicator of urban logistics performance to comprehensively evaluate the development of urban logistics and created an indicator system to evaluate the performance of urban logistics with the input and output of urban logistics and operational efficiency, so as to provide a precedent for future comprehensive evaluation of urban logistics. Zhong Yaoguang and Zhang Zhiyong [53] constructed an evaluation indicator system for joint ecommerce distribution. With fuzzy hierarchical analysis, they assumed that the government aims to improve public services, set the six indicators of resources, capacity, quality, service quality, operational risk controllability and contribution capacity of distribution, and proved that the government can realize the optimal social effect when choosing the joint distribution model mainly employing the third party and aiming to improve public services [53]. The research on the planning for urban logistics system in conducted. Cui Li and He Mingke [54] considered urban logistics planning in the low carbon era to enhance the application of low carbon technology and ensure sustainable development. Wang Hao [55] proposed steps for urban logistics planning in terms of infrastructure, logistics parks, information system and development policy based on the characteristics of urban logistics. Zhou Qian, Zhou Xia, Liu Jun et al. [56] optimized the urban logistics and distribution network based on mobility and reliability, and solved it using the ant colony algorithm. The results showed that the model improved the reliability of urban logistics and distribution [56]. Hu Lin [57] constructed a two-layer planning model for the optimization of site selection of the logistics distribution center, proposed an improved particle swarm optimization algorithm, and proved the feasibility and effectiveness of the method through simulation. Wang Guohua, Liu Jinxia, and Wang Guangxian [58] introduced the theory of spatial distribution of logistics nodes on the basis of the logistics park quantity model based on the logistics quantity and analyzed and calculated the number of logistics nodes at the three levels of logistics centers and distribution centers with logistics parks in China as an example. Long Xingxian, Wu Yao, and Guo Shenghui [59] established a mathematical model for urban logistics and distribution vehicle

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paths in time-varying urban traffic, used the two-stage algorithm to fine the solution, and proved the effectiveness of the algorithm. He Shanshan, Zhu Wenhai, and Ren Qingqing [60] established a multi-objective mathematical model based on optimal time and cost using the relative robust optimization method given the uncertainty of demand in case of unexpected events and verified the optimality of its site-path scheme with uncertain demand. Gong Meng and Qi Chunjie [61] proposed an ideal model for logistics network planning through empirical analysis with Jiangsu Province as an example, and developed a specific plan for urban logistics networks in Jiangsu Province. Zhang Hongda et al. [62] found that the spatial differences in road traffic status, distribution time windows, distribution paths, and urban logistics and distribution demands have the most prominent impact on the temporal reliability of the urban logistics and distribution system, and created a model for assessing the temporal reliability at three levels: logistics and distribution sections, paths, and networks. Yang Jianhua and Gao Huijie [63] established a through-path analysis model for factors influencing carbon emissions in Beijing’s urban logistics industry, pointed out that the construction of logistics infrastructure was the most primary factor to cause the growth of carbon emissions during 1998 and 2012, and provided countermeasures for the low-carbon development of Beijing’s logistics industry. The common modeling methods for the optimization of logistics facility layout include the stochastic planning model, fuzzy planning model, dynamic planning model, multi-attribute decision-making method, and hybrid decision-making method. The solution-finding means mainly include the Lagrangian algorithm, branch-and-bound method, genetic algorithm, simulated annealing algorithm and taboo search algorithm. Domestic and foreign scholars mainly focus on the design of algorithms, mainly with single-objective function and heuristic or evolutionary algorithms [64]. However, the characteristics of the urban logistics system are not studied sufficiently when modeling, parameter setting quite ideal, and further in-depth research to be carried out. Since the 1990s, scholars from Japan, Europe and the United States have started with the research on urban logistics. As far as the current research results are concerned, the research on urban logistics system at home and abroad is mainly found in a certain part of urban logistics system, such as logistics demand forecasting, route planning for logistics distribution, and layout design for logistics networks. Early research mainly focused on urban logistics system planning and design, and recent research on urban logistics and the sustainable development of cities [65]. At present, governments of many countries at home and abroad have realized the important role of the development of urban logistics in the economic development of cities and the enhancement of their comprehensive competitiveness. Relevant government departments have carried out work to ensure the sound and stable development of urban logistics, and paid attention to the planning for urban logistics system. However, the concept of urban logistics has only been proposed for about ten years, theory lags far behind practice, and theoretical research is not intensive enough [66]. Relatively

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1 Introduction

speaking, foreign scholars have conducted few studies of the relationship between urban logistics and cities, and domestic scholars have just started to explore the urban logistics system planning and design methods [67]. In general, the research on urban logistics at home and abroad is still in the initial stage, and more scholars are encouraged to conduct in-depth and systematic research and demonstration. In the planning and design for urban logistics system, there are still a large number of issues that need to be studied and explored in depth, especially in the following aspects. First, the urban logistics system has characteristics that distinguish itself from other systems, such as the limitation of the scope, and the constraints of urban policies and urban development planning. Therefore, a network optimization model applicable to the urban logistics system should be established in system planning. As the urban logistics system is vulnerable to unexpected events and other factors with high uncertainty, volatile factors need to be considered for the prediction of urban logistics demand and the planning for the urban logistics system, so as to build a reliable urban logistics system. Second, there are few studies of the overall planning for urban logistics system. The urban logistics system is a large and complex engineering system that requires reasonable and optimal allocation of its components, so as to achieve the goal of the overall optimal system. Most of the current research focuses on a certain aspect of urban logistics, such as the site selection of distribution centers, optimization of distribution paths, and control of inventory. Although the operational capacity of urban logistics directly affects the development of urban logistics, urban logistics is a complex and dynamic system, and information, policies and regulations, force majeure and other influencing factors should also be taken into account during planning for the system, which play a crucial role in the planning for urban logistics. Third, scholars have no systematic study of the influencing factors of urban logistics. Urban logistics is a complex system, and its smooth operation is affected by many factors inside and outside the system. There are many factors, and intricate links between them, so it is difficult to list all of them and analyze their mechanism of action. Although there are many factors affecting the efficiency of urban logistics operation and the relationship between factors is complex, these influencing factors must be taken into account when conducting optimization studies of urban logistics system. The important factors can be analyzed through the key influencing factor refinement model, and each factor can be improved step by step. Fourth, the relationship between urban logistics and the urban environment should be considered comprehensively when studying urban logistics, and both positive and negative effects should be analyzed. With the increasing urbanization and rapid development of urban logistics, the “urban maladies” are becoming increasingly prominent, such as traffic congestion and environmental pollution. These factors affect not only the life of urban residents but also the development of the whole city. A comprehensive study of urban logistics can help bring out the advantages of urban logistics and eliminate its external uneconomical nature.

1.2 Literature Review

19

1.2.2 System Reliability During the rapid development of science and technology in the modern era, reliability technology has emerged along with the development of production and science and technology. Reliability engineering science is a comprehensive interdisciplinary subject involving many fields, such as mathematics, physics, chemistry, electronics, mechanics, environment, management, and ergonomics, to solve reliability related problems as a starting point. Although reliability techniques were initially developed to adapt to the requirements for the high reliability of products, reliability has been used in various fields with the development of science and technology. Therefore, the definition of system reliability can be interpreted differently in different subjects, but in general, system reliability is defined as the ability of a system to perform a specified function under specified conditions and within a specified period of time. Reliability research was first widely used to assess the reliability of individual electronic components, and extended to the reliability of products in general and to that of more complex associated systems in the 1960s. Reliability research in logistics systems has emerged only in recent years. Now some domestic and foreign scholars have embarked on it. Hamed, S., Ayyoub, B. and Al-Zabin, N. et al. [68] considered the importance of links and cost constraints in system reliability optimization issues, and studied the reliability of complex systems under critical constraints with the genetic algorithm. Xing, Y. Y., Wu, et al. [69] proposed a dynamic Bayesian evaluation method for the timely correction of system reliability growth based on the consideration that the small sample size of field tests is prone to inaccurate estimation in the system reliability assessment. The method directly uses field test data to assess system reliability from all stages of the system development process [69]. Guo, S. X. [70] proposed a robust reliability method for solving the problem of dynamic system reliability based on an appropriate description of uncertainty. The method provides necessary and sufficient conditions for the quadratic stability and stabilization of the uncertain system and is applicable to the edge case with uncertain parameters [70]. Andreja Križman (2011) studied the method of measuring the reliability of logistics structure and the calculating method of its convergent validity. Chaug-Ing Hsu and Hui-Chieh Li [71] analyzed reliability assessment and adjustment for supply chain network design in the presence of demand fluctuations. Mohit Kumar and Shiv Prasad Yadav (2012) used different types of intuitionistic fuzzy numbers to construct the computational functions of intuitionistic fuzzy numbers of membership functions and fuzzy reliability of non-membership functions by means of nonlinear programming techniques. Jianhua Wang, Dan Li, and Difang Chen [72] discussed Bayesian estimation of reliability parameters of systems and property zero failure date of E-Bayes estimation in Bayesian (E-Bayes) estimation and system reliability parameters for multi-layer Bayes estimation. Qian Jin and Zhang Tao [73] proposed an enhanced and extended object-oriented Petri net model for the analysis of reliability of the complex associative system. Yin Xiaowei, Qian Wenxue, and Xie Liyang [74] proposed a BN-based multistate

20

1 Introduction

system reliability modeling and evaluation method using Bayesian networks and then used the probability distribution table 9 to express the states of the association nodes, so as to establish a multistate BN model. It is able to calculate the system reliability directly based on the probability of multiple states of the original [74]. Chen Guohua et al. [75] analyzed the causes of supply chain failures from the three aspects of supplier, manufacturer and distributor, and proposed a supply chain reliability diagnosis method based on fault tree analysis. He adopted the Monte Carlo simulation approach to acquiring relevant reliability indicators and diagnosing key factors [75]. Chen Deliang and Chen Zhiya [76] defined the reliability of logistics network supply points, arc reliability and logistics network reliability in terms of probability and with the measure of customers’ shortage in a logistics system, and constructed a single-layer network reliability model. Later he proposed the logistics network reliability optimization related issue and built a bi-objective opportunity constrained planning model based on service reliability and cost [77]. Jiao Yujie and Mu Dong [78] explored the structural reliability and operational reliability of the logistics system for energy emergency based on the consideration of natural disasters and public emergencies. Chen Cheng and Xue Hengxin [79] constructed a comprehensive assessment model for supply chain reliability based on MAS to ensure the normal operation of the supply chain. Cai Jianming, Li Xiamiao, and Yang Guanghua [80] studied the path selection for emergency logistics transportation after earthquake disasters based on the time-varying nature of the travel time of emergency logistics transportation paths and the time-varying safety reliability. Liu Qin and Sun Linyan [81] proposed a reliability algorithm based on probabilistic importance to assign components, and conducted comparative analysis with existing algorithms. The new algorithm effectively improved the computational efficiency of reliability optimization of complex systems and large-scale systems [81]. Liu Yong and Ma Liang [82] proposed a method for solving the reliability optimization problem for complex systems—hybrid universal gravity search algorithm as its optimal search mechanism can instruct the population for global search. They also adopted the sequential quadratic programming algorithm for local search, and proved by comparing that the algorithm has high feasibility and effectiveness [82]. Ruan Yuanpeng and He Zhen [83] proposed a reliability assessment method integrating opportunity-based Monte Carlo simulation and cellular automata for complex systems while considering the common cause failure of the system caused by component selection propagation. The algorithm broke down the limitation of the traditional method that can only assess the reliability of simple systems, and it can thus be applied more extensively [83]. Yang Lechang and Guo Yanling [84] reconstructed the classical Bayesian inference algorithm by combining direct prior distribution, indirect prior distribution and fused prior distribution, and created a system reliability analysis method based on the fusion of Bayesian inference and information extraction, which is a hybrid Bayesian algorithm based on self-updating weight coefficients. Zhang Yugang, Sun Jie and Yu Tianxiang [85] considered complexity and failure hazard as factors that directly affect system reliability assignment, which are used to identify the relative size of weights in assigning unit reliability. They identified working environment, technology level, and improvement cost as indirect influencing factors, and applied relative dispersion

1.2 Literature Review

21

to calculating complexity and hazard and portraying system component unit failure, and finally created a series system reliability allocation model based on Vine Copula function and considering different failure correlations among the constituent units [85]. Wang Renze et al. [86] created a new algorithm where all components in the common cause failure group are replaced with the equivalent common cause failure components of this group by targeting common cause failure in system reliability analysis as the major difficulty. It can complete all common cause failure groups at once and can be considered a suitable method in this regard [86]. In the field of logistics system reliability research, existing literature focuses on the measure, evaluation and prediction of logistics system reliability, mainly with the methods of Monte Carlo simulation, fault tree and probabilistic analysis, so as to establish an assessment model for logistics system reliability. Certain research results have been achieved. In terms of research trends, scholars are paying more attention to the application of theoretical methods of complexity science to the study of system reliability, and nonlinear programming techniques and intelligent methods are being applied more widely and intensively in this field. The future research on system reliability may be concentrated on the following aspects: First, there is a lack of research on the influencing mechanism of various random factors on system reliability. Various contingencies affect the traffic capacity and demand of the system, such as the change of routes selected and times of travel. During the system reliability research, the mechanism of how factors influence system reliability should be clarified first. Second, system reliability is assessed with the lack of unit reliability and considering failures. The system reliability optimization must be based on the assessment of unit reliability. The failure of some units in case of unexpected events will inevitably have certain effects on the reliability of the whole system, and the mechanism of its effects and consequences should be clarified.

1.2.3 Research Review The urban logistics system is a comprehensive one integrating several functions, with geographical limitations and urban attributes. The urban logistics system serves the urban economy and citizens’ lives and is a service-oriented system that guarantees the normal operation of the city. With its extensiveness in content, large scale and complexity in management, the urban logistics system is the basis of urban resource integration, and its reliability has a significant impact on the smooth operation of the urban economy and society. The study of reliability optimization models for urban logistics system has become a hot issue to be explored with increasing attention from researchers in the field of logistics management. Future research may focus on the following aspects.

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1.2.3.1

1 Introduction

Inadequate Theory of Reliability of Urban Logistics System

The urban logistics system has its own characteristics, with fixed scope and regional restrictions, and is subject to the influence of government policies, urban planning and other factors. The operational status of the urban logistics system is directly related to the development of the urban economy, people’s livelihood and social stability. However, according to the existing research results, there is little literature on urban logistics system reliability, and most of the research focuses on urban logistics system or system reliability, without considering the two combined. The reliability composition, operation, evolution and measurement of urban logistics system have not been covered by theoretical research.

1.2.3.2

Insufficient Scientific and Systematic Models and Methods for Optimizing the Reliability of Urban Logistics System

The urban logistics system is a complex and dynamic one and an organic whole composed of urban logistics distribution, information, storage and other subsystems. The existing literature is basically isolated studies from a certain link or element in logistics, such as urban logistics demand forecasting or urban logistics node site selection. However, parts of the urban logistics system are interdependent, interacting and mutually constraining, and the system is a composite, unified whole composed of all parts. If the urban logistics system is studied only from a certain link, the scientific and overall nature of the studies will be adversely affected and the operation mechanism of the urban logistics system cannot be comprehensively revealed. The research on the design, distribution and optimization of the reliability of the whole urban logistics system from the perspective of synergy is insufficient and cannot reflect the operation and evolution law unique to such a complex system of urban logistics. Urban logistics and urban operation are closely related, the increasing demands brought by urban development provide a proper platform for the logistics industry, and a stable and reliable urban logistics system helps to promote the development of urban economy. The existing optimization models are designed independently only for the sake of reliability or mobility, yet with the synergy between urban logistics and urban economic and social operation neglected. When considering the optimization of urban logistics system, the relationship between urban operation and urban logistics should be clarified first, only based on which the optimization of the logistics system will help drive forward the further development of urban economy.

1.2.3.3

Introduction of Intelligent Algorithms/Intelligence in the Logistics Industry

At present, China’s logistics industry has greeted unprecedented strategic opportunities as a series of major national development strategies have been put forward at the 19th CPC National Congress to point out the direction for developing smart logistics

1.2 Literature Review

23

and promoting high-quality development of the logistics industry. For example, we should adapt to the “manufacturing power” strategy by supporting the transformation and upgrading of intelligent manufacturing and service-oriented manufacturing; conform to the “new pattern of opening up in an all-round manner” strategy by building a smart logistics service network that meets the needs of the Belt and Road initiative and actively integrating into the global supply chain system [87]. In addition, the State Council, the National Development and Reform Commission and other relevant departments have issued policies to support the innovative development of the logistics industry, thereby effectively mobilizing the enthusiasm of employees in logistics, promoting the transformation and upgrading of the traditional logistics industry, and ushering in the “era of intelligence” for China’s logistics industry based on the combination of the Internet big data, cloud, artificial intelligence and other high technology. It is not only an objective need for the development of the industry in the Internet information era, but also a necessary path for China to become an international supply chain powerhouse. The intelligent upgrade of the logistics industry will inevitably lead to that of urban logistics, and the emergence of new products, services, models and other new things. Relevant theoretical knowledge in this regard is yet to be explored and discovered. The reliability of the urban logistics system affects all aspects of daily life. For example, the distribution of essential goods is related to the living quality of urban residents and the survival and development of the city. Reliable distribution can enhance the happiness of residents and contribute to the development of the city. A city is densely populated and occupied with buildings, which requires safety and reliability in the distribution of dangerous goods in the whole process. A slight mistake will lead to inconceivable consequences. Therefore, the importance of the reliability of urban logistics system is evident, and it is worthy of in-depth and meticulous study. Based on the current research on the reliability of urban logistics system, this book will address their shortcomings by combining optimization, simulation and empirical evidence based on the theory of urban logistics and system reliability and the reliability optimization model of urban logistics system, and drawing on theoretical knowledge from system science and complexity science. The details of research are listed as follows. (1)

(2)

Based on the concepts related to urban logistics and system reliability, the meaning, characteristics and composition of urban logistics system reliability are proposed. From the perspective of the urban logistics network structure, the reliability of the logistics system is analyzed, including the node reliability and arc reliability of connected nodes. The reliability of the urban logistics system in terms of its influencing factors and components is studied. The role of the urban logistics system as a whole in influencing urban commerce, people’s livelihood, and transportation is explored based on its constituent elements and their interrelationship. The factors influencing the reliability of urban logistics system are studied. The

24

(3)

(4)

1 Introduction

urban logistics system as a whole will be affected by internal and external environmental factors, such as weather conditions, traffic conditions, emergencies in the city, internal operation, and the degree of information application. With each link of logistics activities as the research logic, including supply, preparation, distribution, storage, processing, and transportation, matter-element analysis is done and the influencing factors withdrawn in terms of information, operational capacity, technical equipment, policies and regulations, and force majeure throughout each node and chain of urban logistics, and the factors affecting the reliability of urban logistics system clarified based on statistical decision-making. Reliability measure of urban logistics system is studied. First, the reliability of the urban logistics system is estimated. The multi-layer Bayesian estimation method is applied to sample reliability assessment according to the statistical characteristics of the urban logistics system reliability. Second, a reliability measure model for the urban logistics system is established. The reliability measure model, method and index of the urban logistics system are clarified from the microscopic point of view. The key elements are withdrawn from the components of urban logistics reliability, and the reliability measure model and method designed to measure the reliability of the empirical samples with the key elements as the main measure indicators. Research on the reliability optimization model for urban logistics system is conducted. Mobility is an important factor affecting the reliability of urban logistics system, and optimization studies are based on the theory of mobility reliability, considering the conditions for random changes in customer demand. The idea of multi-objective planning is drawn on, reliability combined with economy and mobility, and an urban route optimization model designed. The model will reflect the coordinated allocation and development among urban resources, logistics resources and logistics demands.

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Chapter 2

Relevant Theories

Logistics industry is the “third source of profit”. Its development will drive forward that of the whole city and even the region. The city is a gathering place of production, circulation and consumption. From production and life in the city arises a great demand for logistics. Urban logistics serves the city. It is a supporting system of logistics services to meet people’s production and living needs and ensure the smooth operation of the city. With the acceleration of urbanization, the expansion of urban scale and the increase in population density, the scale of urban logistics is also expanding. The series of urban problems caused by urban logistics, such as traffic congestion and environmental pollution, have stressed the particular importance of the planning for urban logistics system.

2.1 Urban Logistics 2.1.1 The Concept of Urban Logistics The formation of urban economy is the prerequisite for the existence of urban logistics and the concentrated reflection of all links of social reproduction in urban space. The expansion of urban scale and the development of urban economy will promote the prosperity of urban commodity market. With the circulation of commodities, urban logistics came into being [1]. In the 1970s, cities became “the gathering place of negative effects of logistics”, so scholars put forward the concept of urban logistics. Taniguchi et al. from the University of Tokyo in Japan defined urban logistics as the process for optimizing the logistics and transportation behaviors by private companies considering the traffic congestion and waste of resources in the traffic environment of a city within the framework of a market economy. Wei Xiujian, a Chinese scholar, believes that urban logistics is a logistics activity system related to operation and supervision in order © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. Zhang, Reliability Optimization of Urban Logistics Systems, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-19-0630-5_2

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to realize the optimal urban commodity circulation under certain urban planning constraints [2]. Specifically, urban logistics is a comprehensive activity to optimize the packaging, loading and unloading, handling, transportation, distribution, warehousing, distribution, processing and other kinds of operation and management of goods within a city by applying advanced information technology under certain urban planning constraints while striving to reduce the negative impact of logistics activities on urban traffic, environment and energy consumption [2]. Urban logistics mainly has six functions: (1) loading, unloading and handling, which refers to the vertical and horizontal movement of goods and also the intermediate link necessary to other logistics activities; (2) packaging, different ways and degrees of packaging in order to ensure the safety of goods and for the sake of transportation, storage and promotion, generally including industrial packaging and sales packaging; (3) storage, one of the two core functions in the logistics system as transportation and such activities as piling up, management, safekeeping, servicing and maintenance to protect the use value and value and facilitate the necessary processing of goods [3]; (4) transportation, another core function in the logistics system, the efficiency of logistics depends largely on the mode of transportation, which needs to be considered from many aspects; (5) information processing, relying on modern information technology and means to ensure the normal operation of the logistics system; (6) circulation and processing, as defined in the Terminology of Standard Logistics of the People’s Republic of China (GB/T 18,354–2006): The general term for operation such as packaging, dividing, measuring, sorting, painting marks, attaching labels and assembling as required in the process of sending goods from the place of production to the place of use. In terms of logistics level, urban logistics can be divided into enterprise logistics, industry logistics and social logistics. Urban logistics can be classified according to the role, scope of activity and subject of the activity. According to the different roles, it can be divided into five kinds: (1) supply logistics, the logistics activities where enterprises organize the supply of all essential goods unremittingly to ensure their own production and business as usual and with the goal of ensuring the on-time, accurate supply of those goods they produce normally and putting the costs under control; (2) sales logistics, service-oriented logistics activities generated in the process of sales activities between enterprises to meet the demand of the buyer and achieve the purpose of sales; (3) production logistics, a logistics activity unique to manufacturing enterprises, resulting from the movement of materials and semi-finished products between processing points in accordance with the process flow, and conducted during the production [3]; (4) recycling logistics, the logistics activities to deal with offcut, scrap materials and defective goods in the whole process from production to sales in order to reduce the waste of resources or pollution; (5) waste logistics, waste materials are sent to a specialized treatment facility for proper disposal according to actual needs as long as it is beneficial to pollution reduction with the ultimate goal of environmental protection only.

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In terms of the scope of logistics activities, it can be divided into three categories: (1) macro logistics, also known as social logistics, that is, the logistics activities for overall social reproduction within the scope of society and oriented to society; (2) meso logistics, also known as industry logistics, with distinctive industry characteristics; and (3) micro logistics, also known as enterprise logistics, which refers to the logistics activities carried out by consumers and enterprises from the macro perspective of the national economy [3]. In terms of the subject of logistics activities, there are the third-party logistics and fourth-party logistics in addition to the well-known seller logistics (first-party logistics) and buyer logistics (second-party logistics). Third-party logistics is also known as contract logistics and defined in the Terminology of Standard Logistics of the People’s Republic of China (GB/T18354-2006) as: special or comprehensive logistics system design or the logistics service model for system operation provided by customers independent of the supply and demand sides. Fourth-party logistics is a supply chain integrator which provides customers with the required set of complete supply chain solutions and thus benefits from it with the use of information technology, integration capabilities and other resources, as well as the supply and demand sides and third-party leadership forces instead of the party of interests in the logistics activities [3]. Urban logistics research covers a wide range of issues, including transportation, handling, packaging, distribution and other aspects, and related to industry, commerce, transportation and environment. For example, the forecast of urban logistics demand is the basis and premise for urban logistics system planning, and aims to grasp the future development trend so as to effectively guide the planning and design for the logistics system. The planning for the urban logistics system should be based on the existing market laws and logistics conditions besides the result of forecast, and also consider the future development needs of the city, the impact on the city’s economy, culture and society, and rationalize the allocation of logistics resources from the overall interests of society, so as to complete efficient urban logistics with minimal social consumption and improve the efficiency of urban logistics. How to evaluate the operational efficiency of urban logistics, in addition to how to improve the operational efficiency of urban logistics, is also worth studying. The planning for urban logistics system involves many departments. There are different subjects of interest in the city with different needs and goals. The planning for urban logistics system should take into account the demands of enterprises, governments, and residents, and unify the interests of all parties to achieve overall optimization of the system. The industry, commerce and trade of the city as well as its economic and social activities should be based on logistics as a platform. As the scale of the city continues to expand and the urbanization rate increase, logistics needs have become more diversified and random, and logistics operation more complicated. Therefore, an efficient and reliable urban logistics system has become an important part to promote urban economic development and improve the living standard of urban residents, and directly related to the efficiency of the city and determine the development of urban economy. Furthermore, in recent years, with the increasing severity of urban problems such as traffic congestion and air pollution associated

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with urban logistics, relevant studies of how to reduce the adverse effects of urban logistics activities have become popular.

2.1.2 Nature and Characteristics of Urban Logistics Urban logistics is on a meso scale in order to ensure that the production and living needs of the city are met, including the flow of goods within the city and the exchange between inside and outside of the city. Compared with logistics, urban logistics has an additional boundary, taking into account the geographical limits and attributes of the city. Urban logistics system planning aims to achieve the overall optimization of the urban logistics system within the city boundary. It is a kind of logistics in the general sense and also needs to consider the geographical limits and attributes of the city. Therefore, the reliability of the urban logistics system should be studied with full consideration of the characteristics of the urban logistics system.

2.1.2.1

Nature of Urban Logistics

Urban development leads to the demand for urban logistics, and urban logistics is the cornerstone of urban development. The development of urban economy brings forward the demand for means of production and livelihood, which promotes the need for urban logistics. There are four main categories of stakeholders involved in urban logistics, namely, enterprise, third party logistics, residents and government, with respective demand orientation. Businesses tend to obtain a higher level of logistics services at a lower cost. With the uncertainty of customer demand, businesses start to seek an increase in frequency of transportation to reduce inventory and meet the changing needs of their customers. Logistics companies seek to complete the logistics tasks given by their customers at the lowest logistics cost. However, logistics companies often face difficulties in their logistics activities in cities due to traffic congestion and other unforeseen events. Residents are the people who work and live in the city. They do not like being affected by those trucks into the streets and causing the traffic congestion although they serve to deliver the necessary goods. Also, they want to minimize urban traffic congestion, noise, air pollution and traffic accidents. The government’s goal is to reduce traffic congestion and improve the environment as much as possible while meeting the city’s need for logistics activities [4].

2.1.2.2 (1)

Characteristics of Urban Logistics

Logistics sees intensive demand, and a large quantity, wide distribution and small radius of nodes. Cities have traditionally been densely populated, and the demand for urban logistics naturally intensive. In Beijing, for example, according to Beijing Statistical Yearbook 2018 [5], as of the end of 2017, its

2.1 Urban Logistics

(2)

(3)

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resident population had been 21.707 million with a density of 1,323 per km2 . The eastern and western parts were the most densely populated, with 20,330 and 24,144 people per km2 respectively, for which the intensiveness of urban logistics demand can be imagined. Urban logistics serves for production and living by not only delivering production necessities for industrial enterprises but also living necessities for residents. Production enterprises, supermarkets, shops and consumers are all distribution nodes of logistics located within the scope of the city, which indicates a small radius. In recent years, with the successive upgrading and modernization of consumption methods, and the rapid development of e-commerce, the form of urban distribution has changed, presenting new characteristics such as the small amount, diversity in species, time sensitivity and door-to-door service. As the center of socioeconomic activities, the city witnesses a large scale in urban logistics information, great fluctuations, and a wide range of information. The origin, processing place, transmission route and use nodes of information are scattered in a wide area and change frequently. Urban logistics covers a wide range of areas, including production, distribution and consumption, and involves every aspect of social reproduction [1]. The scale of urban logistics is affected both by the economic and social development within the city and by the surrounding region. It sees short transportation distances, mainly by road. Urban logistics includes urban input logistics, urban output logistics and intracity logistics. Compared with regional logistics, urban logistics is defined by the scope of the city, and thus does not involve long-distance and large-scale logistics services but mainly focuses on short-distance transportation. City logistics largely relies on road transportation, and partly on shipping and pipeline transportation. It basically does not involve air and railway transportation. Given the small volumes, multiple species, high efficiency and short distances, urban transportation means tend to be small-sized. However, at present, China sees low efficiency and effectiveness of logistics. Enterprises in the city largely use self-owned heavy-duty trucks for the transportation of raw materials. Most are transported one way, and the high rate of empty loads causes a serious waste of resources. The low efficiency of logistics and transportation directly leads to waste of fuel, air pollution, and traffic congestion, and has a negative impact on the entire city [6]. There are many constraints and complex logistics objects. The urban logistics system is one with the city as the platform, and its research focuses on how to allocate resources to meet the needs for urban production and living. Urban logistics has to consider the dual influence of geographical restrictions and urban attributes. Within the limited urban space, commercial tourism facilities, cultural and sports facilities, educational and medical facilities, residential and other buildings and production and living facilities are densely distributed, and the planning for the development of many cities has made corresponding restrictions on the location of logistics infrastructure. These factors will affect and restrict the layout of the logistics network and route selection within the urban space, and the planning and construction of the city

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will generally precede that of urban logistics, so the urban logistics lags behind in development and changes constantly. In addition, many cities have formulated corresponding control regulations on transportation. For example, the restrictions on the passage of large vehicles and specific license plate numbers, and other city-specific characteristics have become the constraints of urban logistics development. The flow of material goods in urban logistics includes not only that of raw materials and equipment required in urban production, but also the means of living in the city and the flow of urban waste [1]. First, logistics personnel have a complex composition. There are suppliers, logistics enterprises, consumers, and also government departments. Second, there are complex and many kinds of objects, including agricultural and sideline products, industrial semi-finished products and finished products. These objects have different requirements for logistics. Therefore, it is necessary to study the reliability of urban logistics system by comprehensively considering its characteristics of a large scale, a large quantity of objects and a complex structure. Urban logistics is a bridge connecting production and consumption. The elements in the system are vulnerable to external natural, social and economic influence. The fragile urban logistics system may render the whole city malfunction. Therefore, the urban logistics system should remain flexible and reliable in case of emergencies and have its ability improved to deal with sudden changes in demand. (4)

Urban logistics is closely related to intracity businesses. Urban logistics and enterprise logistics affect each other, and are inseparable [7]. Urban logistics is carried out city-wide and composed of all enterprise logistics in the city. The logistics activities of each enterprise are an integral part of urban logistics. Therefore, some logistics functions and logistics infrastructure fall under both enterprise logistics and urban logistics. For example, the storage of raw materials, semi-finished products and finished products of enterprises can be regarded as that both for enterprises and cities. An efficient and reliable logistics distribution system will not only enable enterprises to lower logistics cost and increase production efficiency, but also effectively alleviate the pressure from urban traffic congestion. Likewise, the transportation function, distribution function, loading and unloading function, packaging function, circulation and processing function and information processing function of logistics can be regarded as those of enterprise logistics and urban logistics. As a node of logistics, city plays the role in distributing and transferring goods. The logistics activities of enterprises are integrated into those of the city, and then serve the internal needs of the city and communicate with the outside of the city. Enterprise is the prerequisite for the existence of urban logistics. Urban logistics is the link between enterprise and the external, and facilitates the exchanges between enterprise and external environment [6].

The operation of urban function is inseparable from urban logistics, which can reasonably allocate resources. Urban logistics system is complex and dynamic, and

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its operation affected by urban development planning, urban road network planning and urban policy. There is a “trade off” contradiction—as a certain functional element in the logistics system is optimized and benefits occur, the efficiency of another or several other functional elements will be reduced. If not handled properly, it will lead to the low efficiency of the whole logistics system and eventually damage the interests of the whole urban logistics system. When studying the reliability of urban logistics system, we should comprehensively consider the needs of various elements and seek the optimization of the whole system. The rational planning for urban logistics infrastructure is not only an important prerequisite and necessary foundation for the effective functioning of urban logistics system, but also related to the development of the whole urban economy and the living standard of urban residents. The industry and trade involved in urban logistics activities are also closely related to urban development and planning. With the acceleration of China’s urbanization process, it is urgent to establish an efficient and reliable urban logistics system, which not only promotes the normal operation of urban functions, but also is of great significance to alleviating traffic pressure, ensuring people’s livelihood and propelling urban development. Therefore, to explore the operation law of urban logistics system as a whole in terms of its constituent elements and their relationship is the premise and foundation of reliability optimization model and method research [8].

2.1.3 Development Model of Urban Logistics According to the characteristics of modern logistics and considering the city’s economy, location and other factors, the development mode of urban logistics has several development modes, such as industry-driven, business-driven, transportation hub-driven and port-driven [1]. Cities in different regions have different economic structures, roles and places in national economy.

2.1.3.1

Industry-Driven

Industrial cities refer to those with well-developed industrial economy, such as the steel industry, heavy chemical industry, mining industry and machinery manufacturing industry. Urban logistics should build a service platform for industrial enterprises’ products, and guide storage, transportation and distribution enterprises for playing a synergistic role. By improving the comprehensive utilization efficiency of social resources and logistics level for industrial raw materials, semi-finished products and finished products, urban development will be advanced. For example, Fushun, China’s “coal capital”, has shifted from a coal mining city to a comprehensive industrial city with petroleum, iron and steel, machinery, aluminum refining and electric power industries. The industrial base in central-southern Liaoning is the

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most important and largest among heavy industry bases in China. The area is rich in mineral resources, mainly coal and iron, and petroleum. The convenient transportation and excellent industrial foundation are all helpful to the development of heavy industry. The prosperous development of each industrial base cannot be separated from major industrial centers (cities). The iron and steel industries of Anshan and Benxi, the machinery industry of Shenyang, and the shipbuilding industry of Dalian are the backbone of China’s heavy industry. In Shenyang for example, the industrial construction and transportation development are complementary. As one of the railway hubs in northeastern China, Shenyang is the intersection of several railway trunk lines, including the BeijingHarbin Railway, Shenyang-Dalian Railway, Shenyang-Jilin Railway and HarbinDalian High Speed Railway. In addition, Shenyang Taoxian International Airport, one of the eight regional hub airports in China, is also a shipping hub in northeastern China, 20 km from the city center in the southern suburbs of Shenyang, and a portal of northeastern China to the outside world.

2.1.3.2

Business-Driven

Commercial cities are central ones responsible for the circulation of goods in a certain region, such as Guangzhou and Shanghai in China. The large population and the concentration of regional commercial activities provide good conditions for the development of urban commerce [9]. Commercial cities are generally larger commodity production bases in a region, with a great power of consumption, a well-equipped commodity circulation system, facilities and means of information exchange and convenient transportation conditions. The development of such cities can be promoted through the rational organization of intracity commercial logistics activities. In Shanghai, for example, the total output value of its tertiary industry in 2000 was 8.5 times that in 2017, and increased from 250.354 billion yuan in 2000 to 2119.154 billion yuan in 2017. The mileage of railway in Shanghai increased by 81% from 257 to 465 km, and that of road by 123% from 5970 km to 13,322 km.

2.1.3.3

Transportation Hub-Driven

Transportation hub cities are located at the intersection of comprehensive transportation networks, with multiple modes of transportation and important routes that bridge cities, like Beijing, Tianjin, Shanghai, and Zhengzhou. These cities enjoy wellequipped infrastructure and great capacity in transportation, so their transportation advantages should be brought to full play in development, their routes optimized and transportation efficiency improved. The characteristics of transportation hub cities should be used to drive forward the development of the city. Zhengzhou for example, where most of the major stations to constitute Zhengzhou Railway Hub are located and many of China’s railway arteries is a major traffic

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hub linking north and south and east and west and situated at the center of several large and medium-sized railway hubs in China, such as Beijing Railway Hub, Jinan Railway Hub, Xi’an Railway Hub, and Wuhan Railway Hub. It has a superior position in the national railway network and is known as “the heart of China’s railway”. Zhengzhou Railway Hub has complete infrastructure. In Zhengzhou city alone there are three high-speed railway stations, one ordinary railway station, one freight station, and one marshalling station. Thanks to the well-equipped infrastructure and great organizational transportation capacity, Zhengzhou Railway Hub ranks among the top across China in terms of passenger and freight traffic, transit operation, and train formation and decoupling.

2.1.3.4

Port-Driven

Port cities are those located along rivers, lakes, oceans, and other kinds of waters, with ports and functions as that of land and water transportation hubs, such as Shanghai, Dalian and Guangzhou in China. These cities have excellent geographical locations and port conditions. Their advantages in port collection, storage and distribution of goods should be highlighted and a comprehensive port service system developed that covers all links of the logistics industry chain based on port-side industries, supported by information technology, and with the goal of optimizing the integration of port resources. Its influence on the surrounding logistics will be strengthened, the efficiency of logistics organization improved and the development of the city promoted. In Dalian for example, the city was built and prospered based on ports. The port of Dalian is strategically located in the center of the Northeast Asian economic circle and is the maritime gateway of northern China to the Pacific Ocean and outside world. The port of Dalian ranks among the top at home and abroad in crude oil, ore, automobiles, grain and handling capacity of passenger terminals, and is in the leading position with complete supporting facilities and services. The rapid development of Dalian port has effectively promoted the development of the city and its supporting infrastructure: In shipping, one can travel from there to other cities in China such as Yantai, Weihai, Penglai and Tianjin, and Incheon, South Korea. In railway, there are many stations of the Shenyang-Dalian railway in Dalian, and the Harbin-Dalian highspeed railway and the Dandong Express Railway have also been put into operation and contributed to the development of Dalian. In highway, the Shenyang-Dalian Expressway, for example, connects the port of Dalian with the national highway network in northeastern China, and many cities on the Liaodong Peninsula, so as to facilitate the development of the port of Dalian, Dalian and even the entire Liaodong Peninsula.

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2.2 System Reliability 2.2.1 Overview Reliability is an important engineering discipline. The emergence of reliability engineering is inseparable from the needs of society and the development of science and technology. The related research on reliability originated during the Second World War. The complexity of military technical equipment led to high failure rate and hence the start of research on reliability. Although its research first began in the military field, reliability engineering has become widely used in many other fields and played an important role in improving the economic benefits of enterprises with its continuous development. China started late with the reliability engineering research and still sees a big gap from developed countries, despite its rapid development..

2.2.1.1

Basic Concepts of Reliability

According to the national standard GB-6583, reliability refers to the ability of a product to perform a specific function under specified conditions for a specified period of time. The reliability study includes not only equipment and components but also systems. According to the definition of reliability, the three elements of reliability are “under specified conditions”, “for a specified period of time”, and “to the extent that the specified function is achieved”. Important tools for studying reliability engineering include probability theory and mathematical statistics, so much reliability theory is closely related to the knowledge of probability theory. “Specified conditions” refer to the environmental conditions and working conditions during the use and operation of the product or system, including climatic conditions and physical conditions. For example, cars of the same model show different reliability performance when running on the highway and on the rugged mountain road, so the conditions must be specified when studying reliability. “Specified time” refers to the task time specified for the product. With the increase in the task time for the product, the probability of product failure will increase and its reliability decline. Therefore, it is necessary to specify the task time when studying reliability. For example, a car has been used for 5 years sees obviously much higher probability than that when it just leaves the factory. “Specified function” refers to the required function and technical indicator of the product. The number of specified functions and the technical indicators directly affect the reliability indicators of the product. For example, the technical indicators of a vehicle include power performance indicators, fuel economy indicators, handling stability indicators and ride comfort indicators. Different reliability indicators are used depending on whether all these indicators or only one or two need to be considered [10]. The reliability of a system is defined as “the probability that a unit can complete a specified function under given conditions and within a given period of time” [11].

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The reliability of a network is defined by assuming that a graph consisting of some nodes and arcs becomes a network S. Let x be any segment of an arc in the network S. The “system normal” event is denoted as S, and the “arc x failure event” as x. In a given network S, the probability of a segment of arc x working properly at a fixed moment T is Px = P(x). The reliability of a network can be defined as the probability that the system S is normal at moment T, i.e. RT = P(S).

2.2.1.2

Principle and Content of Reliability Research

From another point of view, reliability is the failure rate of a structure. Failure rate refers to the probability of failure per unit time of the product that has not failed at a certain time. The higher the failure rate is, the lower the reliability is. The lower the failure rate is, the higher the reliability is. The basic methods to obtain the failure rate include direct integration, numerical simulation and non-probabilistic uncertainty. Direct integration is a method to do the integration by transforming the integral into the form in the basic integral table through algebraic or triangular identical transformation based on the basic integral table and basic algorithm. This method is suitable for a simple structure. It is usually a means of analyzing problems and not directly applied to practical engineering. Numerical simulation is to study engineering problems, physical problems and even various problems in nature with computer and by means of numerical calculation and image display. It has the highest application rate of calculating failure probability. Numerical simulation method, based on simple principle and with reliable analysis results, is applicable to practice. Nonprobabilistic uncertainty is a new method suitable for the failure rate analysis of data samples with a large volume, high price and insufficiency. With the aggregation model, the uncertain factors are input to obtain the range of the uncertainty. In addition, the methods to find out the failure rate also include fuzzy uncertainty, approximate analytical method, numerical integration method, and so on. As an emerging discipline, reliability has its own system, methods and techniques. It includes the following three branches of research. (1)

Reliability engineering. Reliability engineering refers to the technical solutions and organizational management measures that should be taken to ensure that a product or system achieves its intended reliability function in the design and operation process. Reliability techniques are used throughout the life cycle of a product and the whole process of system operation. The first branch is reliability design. A foundation is laid for system reliability with initial design. The reliability model is established to predict the reliability of a product or system, analyze the failure mechanism, and design the reliability on that basis. The second branch is reliability test. The reliability of a product or system is tested through simulation. The reliability of a product or system within a certain period of time and certain cost is studied, so as to find out the weak links and make improvement accordingly.

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The third branch is operational phase reliability. The system reliability during system operation is maintained, early faults removed and defect handling studied. High reliability is maintained by monitoring, predicting, and diagnosing the reliability of the system during operation. The reliability of the whole system is maintained, and the highest reliability with a certain cost and within a certain time frame achieved. Alternatively, cost and time are reduced while meeting requirements for the specified reliability function. The reliability of the whole system process and each link is organized and coordinated. Reliability physics. The study of reliability physics first began in the 1960s when semiconductor devices experienced rapid development and frequent failures, mostly due to physics related causes. The physics of failure thus emerged. Reliability physics is mainly used to deal with the unreliability of products in terms of their mechanism and to provide a basis for the development of products with high reliability. Reliability mathematics. The study of the statistical patterns of product failures that occur resorts to the mathematical and statistical methods that mainly involve techniques of reliability prediction, analysis, design, and evaluation for a product or system. The study of mathematical and statistical methods available in techniques for reliability design, analysis, prediction, evaluation, allocation, and acceptance examination in a product’s life cycle and the whole process of system operation. For example, a space shuttle is composed of parts such as the orbiter, booster, and external storage tank, each of which consists of hundreds or thousands of primary elements. A problem with any one of these components could cause a serious accident. Another example is that a problem in a certain part of the urban logistics system will lead to the collapse of the whole system and affect the basic production and life in the city [12].

2.2.1.3

Significance of Reliability Research

Reliability research has important theoretical and practical significance. (1)

(2)

Reliability research can reduce the probability of accidents. The reliability of products and systems not only affects the development of enterprises but also poses a threat to personal safety. In 1996, China launched a Long March 3B for the first time, and approximately 22 s later, its head crashed. Following a violent explosion, the vehicle was destoryed and casualties caused [13]. With the study of the reliability of products and systems and weak points identified, the probability of failure can be reduced. Reliability research can effectively help reduce the total cost. To improve the reliability of a product or system, it is necessary to invest much money for simulating the environment, performing reliability predictions, analysis and experiments, and so on. However, after the study, the product or system reliability has been improved to a large extent, so as to effectively reduce the maintenance and downtime costs, avoid unnecessary financial losses, and hence reduce the

2.2 System Reliability

(3)

41

total cost. Despite the high upfront investment in reliability engineering, it is essential considering the total cost and service efficiency. Reliability research can help improve system utilization. The improvement of product reliability means a lower failure rate and less probability of downtime, and thus plays an important role in improving system utilization. For urban logistics system, improving the reliability of logistics systems can efficiently serve urban industry, commerce and people’s livelihood, improve the urban transportation environment, enhance urban core competitiveness and promote the development of urban economy [10].

Reliability research has been widely used in many fields, but only recently in logistics system. Logistics network reliability refers to the ability of the logistics network system to fully complete the logistics functions such as normal transportation, warehousing, loading and unloading, and handling in the actual continuous operation process. The ability of logistics network system to resist impact refers to that to provide corresponding logistics service functions after failure [11].

2.2.2 Reliability Feature Vector Reliability feature vector is an assembly of various reliability indicators used to indicate the overall reliability of a product. Reliability, failure rate and other indicators are reliability feature vectors.

2.2.2.1

Reliability

Reliability is the probability that a product performs a specified function under specified conditions and within a specified period of time [11]. It is generally denoted as R. The reliability function of a system is denoted as. R(t) = 1 − F(t) =

N − n(t) N

(2.1)

In terms of a probability distribution, it represents the percentage of the total working parts that work without failure to perform the specified function under the specified conditions and within the specified time.

2.2.2.2

Failure Rate.

The failure rate is the probability, denoted as λ(t) [14], that a product that has not failed when working up to moment t will fail in the next unit of time after t. The estimate of the failure rate is the ratio of the number of products that fail in the next

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unit of time after moment t to the number of products that have worked up to that ˆ moment and have not failed, denoted as λ(t). There are N products that start working from t = 0. The number of failures of the product at time t is n(t), and that at time (t + t) is n(t + t), that is, n(t) = n(t + t) − n(t) products fail in the time interval [t, t + t], then the average failure rate of the product in the time interval [t, t + t] is defined as: λ(t) =

n(t) n(t + t) − n(t) = [N − n(t)] · t [N − n(t)] · t

(2.2)

ˆ is: The estimate of the failure rate λ(t) ˆ λ(t) =

number o f pr oducts that per unit time in(t, t + t) n(t) = number o f paoducts still wor king nor mally at time t (N − n(t))t (2.3)

2.2.3 System Reliability and Calculation 2.2.3.1

Logistics Unit Reliability

The logistics system is organically composed of several logistics units, and the study of the reliability of the logistics system is based on the reliability of the logistics units. The reliability of a logistics unit is the probability that the logistics unit provides logistics services that remain within a specified allowable deviation at a specified time and under specified conditions. As shown in Fig. 2.1, the deviation allowed for the logistics unit service is indicated by that between the dashed lines, and the curve

Fig. 2.1 Logistics unit service capacity curve. Source Xiaochuan and Jianhua [15]

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43

indicates the variation in the actual logistics service capacity of the logistics unit [11]. (1)

Logistics reliability oriented to service quality [11]. 7R theory is the most traditional one in the service quality oriented logistics reliability analysis, and indicates the delivery of the right goods with the right quality, the right quantity, and the right price to the right place and to the right customer at the right time. Definition: Q = {I1 , I2 , I3 , I4 , I5 , I6 , I7 }—a collection of evaluation indicators; Q ∗ (I j ), j = 1, 2, . . . , 7—the value specified for the j-th indicator; Q(R j ), j = 1, 2, . . . , 7—the actual value of the j-th indicator in period T. Then, the service quality reliability of the j-th indicator in examination period T

is: Rj = (2)

Q(I j ) Q ∗ (I j )

(2.4)

Reliability of logistics unit based on time [11]. It is unreliable if the deviation of the service level of the logistics unit in the interval (t1 , t2 ) and (t3 , t4 ) in period T is beyond the specified range.

Definition: t j1 , t j2 , j = 1, 2, . . . , n—starting and ending time when the output deviation of the logistics unit exceeds the limit observed in the j-th time: n— number of observations. Then the reliability of the logistics unit U is: n Ru = 1 − (3)

j=1

T

(2.5)

Reliability of logistics unit based on quantity. Examine the region A1 enclosed by the curve and S = q0 and calculate its area (assuming S(t) is continuously integrable in (t1 , t2 )) t The reliability of number of units of A1 = t12 S(t)dt within (t1 , t2 ) is:  t2 Ru = 1 −

2.2.3.2 (1)

(t j2 − t j1 )

t1

S(t)dt

2 × q0 × (t2 − t1 )

(2.6)

Mathematical model of reliability [14]

Reliability of the series system. A series system refers to that in which the failure of any one of all the component units will result in a system failure. The block diagram for the reliability of a series system is shown in Fig. 2.2.

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U1

Um

Fig. 2.2 Block diagram for the reliability of a series system

Assuming there is a system S with n units, “system S reliable”, is denoted as  S , “unit i reliable” is denoted as i , “unit failure” is denoted n as i , and the system j. normal operation event is the intersection of i :  S = i=1 The reliability of the series system is: R S = P[



 j ] = P{1 }P{2 |1 } . . . P{1 |i−1 , i−2 , . . . 1 }

(2.7)

i=1

P{1 |i−1 , i−2 , . . . 1 } denotes the conditional probability that unit i is also valid when units 1 to i−1 are all valid. The logistics system with a series structure is simple in composition, and easy to coordinate and control. However, a problem in any link can bring the whole system to a standstill. The more units are connected in series, the lower the reliability of the system is. Therefore, to improve the reliability of the series system can simplify the system design and reduce the number of units in the system, and also improve the reliability of the weak units in the system [12]. (2)

Reliability of the parallel systems. It refers to the system that fails only when all the units that make up it fail. The block diagram of a parallel system is shown in Fig. 2.3.

The failure event of the system is the intersection of the unit failure events i , and when the individual unit failure events i (i = 1, 2, . . . , m) are independent of each other, the mathematical model for the reliability of the system is: Rs (t) = 1 −

n  t

Fig. 2.3 Logic structure of a parallel system

[1 − Rs (T )]

(2.8)

2.2 System Reliability

45

Fig. 2.4 Model of a compound system

The parallel structure greatly reduces the requirements for the logistics units that make up the logistics system and allows greater flexibility for the whole logistics system. However, this structure has a lower utilization of logistics resources and high logistics costs. (3)

Reliability of the compound system. The compound system is divided into the series–parallel system and the parallel-series system, as shown in Fig. 2.4.

The series–parallel model consists of m subsystems connected in series, and the components within each subsystem are connected in parallel. Let there be n identical components in parallel within the i-th subsystem, and the system can function properly if at least one component within each part of the system functions normally. If the reliability of each component is Ri j (t)(i = 1, 2, . . . , m; j = 1, 2, . . . , n), respectively, then the reliability of the series—parallel model is: R(t) =

m  i=1

{1 −

n 

[1 − Ri j (t)]}

(2.9)

j=1

A parallel-series system is composed of m subsystems connected in parallel, with the internal components in each subsystem connected in series. The system functions well if at least one subsystem is normal. The reliability of the parallel-series model is:

46

2 Relevant Theories

R(t) = 1−

m 

{1 −

i=1

(4)

n 

[1 − Ri j (t)]}

(2.10)

j=1

Reliability of the network system. In addition to the series and parallel models, there are also models to study the reliability of network system, such as that for the reliability of traffic network system. According to the standard, the network system can be divided into node failure and node non-failure models [16].

First, node non-failure model. A network system consists of nodes and arcs between nodes. Assume that the arcs are independent of each other and have only two states: normal and failed; the reliability is 1 when nodes do not fail. The problem can be described as follows: Assuming that G is a network, and ri , r j are two nodes in the network, find the probability to r j from ri . Second, node failure model. The node failure model uses graph theory as a research tool, where the system consists of nodes and edges, and both nodes and edges have the probability of working properly and not. The reliability of the node failure network in general includes the survivability and the invulnerability of the network. Network survivability indicates a network’s probability of being connected under the random action of disruption with a certain probability of failure in its nodes or links. It studies the effect of network topology and random disruption on the reliability of the whole network. Survivability is not only related to the topology of the network. Network invulnerability refers to the ability of the network with completely identified topology to maintain connectivity under the influence of deterministic destruction, and the number of nodes or links in the network that need to be destroyed at least when the connection between certain nodes is interrupted. Invulnerability is only related to network topology [17].

2.2.4 System Reliability Failure Analysis The structure of urban logistics system has become more complex and its function more improved, so there are increasingly higher requirements for reliability. There are many influencing factors causing system failure, so system failure analysis has become an important part of the study of system reliability.

2.2.4.1

System Fault Tree Analysis

The fault tree analysis (FTA) technique was developed by Bell Laboratories in 1962 as one of the important analysis methods for system reliability. Fault tree analysis, with the most pessimistic failure mode of the analyzed system as the target, aims to locate all the influencing factors that directly lead to the occurrence of the failure, and then all the factors that directly influence the occurrence of the event at the next

2.2 System Reliability

47

level. With the analysis layer by layer, all factors have been discovered and no more will not be found. It involves qualitative analysis and quantitative analysis. The main purpose of qualitative analysis is to find the combination of causes and reasons that lead to undesired occurrences related to the system, i.e., to find all the failure modes that lead to the top event [14]. The main purpose of quantitative analysis is to find the probability of the top event and other quantitative indicators when the probability of occurrence of all bottom or basic events is given. Fault tree analysis can help identify the hazards of various systems and analyze not only the direct cause of failure but also the underlying cause. The fault tree includes the following: (1) possible catastrophic failures of the system, i.e., identification of top events; (2) inherent or potential risk factors within the system, including those due to human error; and (3) connections and constraints between various subsystems and elements, i.e., the logical relationship between inputs (causes) and outputs (results), marked with special symbols. Fault tree analysis is expressed in the form of a fault tree diagram, which is a logical cause-and-effect diagram and shows the state of a system (top event) based on component states (basic events). A graphical “model” path is used to represent a “model” of a system starting from a predictable or unpredictable failure event with logical symbols to associate the basic events.

2.2.4.2

Construct of a Fault Tree

There are three phases in conducting the fault tree analysis, namely, compiling a fault tree, quantitative and qualitative analysis, and developing preventive countermeasures and improving the system. It is shown in Fig. 2.5. Familiarity with the status and all parameters of the system under analysis is the premise for successful application of fault tree analysis. Also, the past and possible accidents of the analyzed system shall be counted, and the accident with serious consequences and frequent occurrence shall be regarded as a top event. Then all the causal events and all the factors related to the accident are investigated, and the events as direct causes found out from the top events, until the requirement for the depth of the analysis is met. The fault tree is then drawn according to the logical relation, and the structural importance of the basic events determined from its structure. According to the results of accident counting and analysis, the probability of each accident is obtained, and then the probability of top event calculated to complete the fault tree analysis. Fault tree analysis reveals the direct influencing factors of existing faults and potential faults by comprehensively and vividly describing the various factors leading to disastrous failures and their logical relations. Based on quantitative and qualitative analysis, it helps discover potential hidden dangers in the system, identify system defects, and provide basis for improving safety design, formulating preventive measures and taking management countermeasures.

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Fig. 2.5 Steps of fault tree analysis

References 1. Fengyi Y. Urban logistics system layout research. Nanjing: Southeastern University; 2005. 2. Xiujian W. Modern logistics and supply chain management. Xi’an: Xi’an Jiaotong University Press; 2008. 3. Jingmin Z. Logistics operations management. Beijing: China Fortune Publishing House; 2015. 4. Changzheng Z. The problems and countermeasures of urban logistics in China. Logist Technol. 2013;32(5):36–8. 5. Beijing Municipal Bureau of Statistics. Beijing Statistical Yearbook 2018. Beijing: China Statistics Press; 2018. 6. Taitian M. Theoretical study on Urban logistics planning. Xiangtan: Xiangtan University; 2005. 7. Yonglan C. Research on urban logistics resource integration based on grid management. Beijing: Beijing Jiao Tong University; 2009. 8. Suxin W. Urban and regional logistics. Shanghai: Shanghai Jiaotong University Press; 2009. 9. Hao Z, Haoxiong Y, Jinlong G. Multi-layer Bayesian estimation model for supply chain network reliability. J Syst Sci Math Sci. 2012;32(1):45–52. 10. Shuangxi K. Research on reliability evaluation of integrated process production system based on material analysis. Chongqing: Chongqing University; 2012.

References

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11. Zunguo H. Application of reliability theory in logistics distribution. Changsha University of Technology Changsha; 2011. 12. Kai Y. Decision model and empirical study of Urban logistics center planning. Changsha: Central South University; 2008. 13. Long March 3B rocket failed to launch. Aerospace China, 1996;(03):28–29. 14. Mingyin L, Renping XL. System reliability. Machinery Industry Press Beijing; 2008. 15. Xiaochuan Y, Jianhua J. Reliability and optimization of logistics systems. J Ind Eng Eng Manage. 2007;1: 67–70. 16. Bo G, Xiaoyue W. System reliability analysis. Defense University of Technology Press Beijing; 2002. 17. Jiping, W. Reliability study on reliability of urban road network systems. Xi’an: Chang’an University; 2005.

Chapter 3

Meaning of the Reliability of Urban Logistics System

Urban logistics plays a vital role in the development of urban economy. Urban logistics must be managed with systematic analysis and research, and a reliable urban logistics system built to meet the ever-increasing demand for urban logistics and ensure the stable development of urban economy. The reliability of the urban logistics system has not only the basic characteristics of logistics but also its own. An efficient urban logistics system combines logistics activities such as transportation, warehousing, loading, unloading, handling, packaging, distribution, circulation and processing, and information processing with production and consumption links to effectively allocate resources, thereby reducing total social costs, alleviating urban traffic pressure, and promoting the rapid development of urban economy.

3.1 The Concept of Reliability of Urban Logistics System 3.1.1 Urban Logistics System 3.1.1.1

Meaning

The urban logistics system refers to a whole that is composed of logistics enterprises, logistics infrastructure, logistics objects, logistics information, and other elements in the city within a certain time and space and functions to organize the urban logistics functions [1]. It mainly covers: logistics infrastructure equipment such as logistics nodes, logistics routes, packaging equipment, handling, and loading and unloading equipment; logistics information platform; logistics management, etc. Urban logistics is a complex and dynamic system that involves all social and economic aspects and has close connections with enterprises, consumers, government departments, and other industries. Compared with the logistics system, it has one more boundary, geographical restrictions and unique attributes of the city. The urban logistics system © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. Zhang, Reliability Optimization of Urban Logistics Systems, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-19-0630-5_3

51

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3 Meaning of the Reliability of Urban Logistics System

Fig. 3.1 Basic model of an urban logistics system

has the three functions of input, conversion, and output like a general system. It exchanges with the social environment through input and output, and is mainly affected by the internal environment and external environment (laws and regulations, fiscal policies, social environment, information technology, etc.). It also exerts an impact on the external society, economy, and environment, and has the output-input feedback mechanism. Its basic model is shown in Fig. 3.1. Infrastructure elements, information platform elements, and policy elements are the three major elements of an urban logistics system [2]. (1)

Infrastructure elements. According to the degree of logistics movement, the logistics process involves movements and relative pauses. The pause in the process of logistics movement connects different types of movement, so “movement-pause-movement” constitute a logistics activity. The logistics infrastructure is composed of two parts: the route for movement and the node for pause.

According to nature, urban logistics nodes can be divided into logistics infrastructure nodes and logistics enterprise nodes. Logistics infrastructure nodes refer to all those for material transfer, collection and distribution, and storage and transportation, such as ports, airports, highway hubs, large public warehouses, modern logistics centers, and logistics parks. They are the knots connecting logistics routes in the urban logistics network. A logistics enterprise node refers to the economic organization that engages in logistics activities, at least a certain area of transportation (including transportation agency and cargo delivery) or warehousing. It can organize and manage such basic functions as transportation, storage, loading and unloading, packaging, circulation and processing, and distribution, according to customer needs for logistics. It an economic organization that has an information management system compatible with its own business, and implements independent accounting and undertakes civil liability independently. It includes the transportation logistics enterprise, warehousing logistics enterprise, and integrated service logistics enterprise. Logistics enterprises are specialized organizations that undertake urban logistics activities and play an important role in optimizing resource allocation and promoting the development of urban logistics. The logistics channels in cities can be divided into internal channels and channels to the outside. The latter facilitates the

3.1 The Concept of Reliability of Urban Logistics System

53

communication between and connect the city and surrounding areas, and the internal channel serves the needs inside the city [3]. (2)

Information platform elements. In the context with rapid development of the Internet and e-commerce, people have imposed increasingly higher demand for order tracking. The timely and accurate order status update has become one of the critical factors influencing customer satisfaction. At the same time, timely and precise logistics information helps companies better control inventory, respond to emergencies, even reduce the probability of unfavorable situations, and hence save costs. For that, people are paying more attention to logistics information that was previously neglected.

The logistics information platform provides services for the interaction or exchange of logistics service supply and demand information. It is to coordinate and integrate logistics-related information based on computer technology, and to provide basic information services for logistics service supply and demand sides. A professional logistics information service website is a typical logistics information platform in daily life, such as chinawutong.com and 56qss.com. The logistics information platform provides decision-making basis for enterprises and individuals by collecting, processing, and outputting multi-party logistics information. It guides, coordinates, and guarantees the normal progress of their logistics activities, and thus helps improve the work efficiency of logistics participants and increase the work efficiency of the entire society. (3)

Policy elements. Logistics policies refer to the policies and regulations formulated by the state or local government to promote urban logistics development and ensure the sound and orderly operation of modern logistics. There are mainly infrastructure supply policies, management and inducible policies, and economic policies. For example, the Medium and Long-term Planning for the Logistics Industry (2014–2020) (2014) issued by the State Council, the Special Action Plan for Cost Reduction and Efficiency Increase for the Logistics Industry (2016–2018) (2016) published by the National Development and Reform Commission, and the Opinions on Promoting the Coordinated Development of E-commerce and Express Logistics (2018) issued by the General Office of the State Council Policies have effectively improved the current situation of and pointed out the direction for the future development of the logistics industry in China.

3.1.1.2

Hierarchical Structure

The urban logistics system is a complex giant system composed of human resource, material, financial, and information elements, and involves an enormous flow of logistics, capital, and information. The various elements of the urban logistics system are interconnected, influence each other, and constitute the structure of the urban logistics system. According to the specific characteristics of the urban logistics, its structure can be expressed as a pyramid as shown in Fig. 3.2 [2].

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3 Meaning of the Reliability of Urban Logistics System

Fig. 3.2 Hierarchical structure of urban logistics system

The goal of the urban logistics system is to improve the modern logistics service network system, form a logistics node network system with a reasonable layout and a comprehensive transportation system supporting multiple transportation modes, create an urban logistics environment featuring “smooth flow of goods”, and improve the overall competitiveness of the city and the living quality of urban residents. Specifically, it is to promote the development of urban industrial clusters, reduce urban logistics costs, and drive forward the development of urban economy; provide a favorable logistics environment for the production and operation of enterprises; improve the urban environment, ease the pressure on urban traffic, and enhance the core competitiveness of the city. The market layer of the urban logistics system refers to the market served by urban logistics. It mainly serves the city’s logistics needs for production and living and the circulation of goods between cities. The functional layer of the urban logistics system refers to the specialized logistics services provided by urban logistics, including transportation, storage, loading and unloading, handling, packaging, circulation and processing, distribution, and information processing. These essential functions are effectively combined and coordinated to form the overall function of urban logistics and enable the entire system to achieve the overall goal in a reasonable and efficient manner. The operation layer of the urban logistics system is the main body to promote the whole system. It is the working process of logistics activities for planning, organizing, coordinating and controlling. Enterprises diversified in nature and business operation constitute the main body of the urban logistics system. Third-party logistics enterprises connect manufacturing enterprises, trade enterprises, and other entities of different nature. These enterprises run in an coordinated manner and seamlessly connect the various types of operation from production of goods to delivery to consumers, so as to improve customer satisfaction and save transaction costs.

3.1 The Concept of Reliability of Urban Logistics System

55

The base layer of the urban logistics system is the platform on which urban logistics activities can be carried out. The base layer includes the hardware platform and the software platform that consists of a logistics infrastructure platform, an information network platform, and a policy environment platform. Well-equipped logistics infrastructure and equipment, an efficient logistics information platform, and a sound logistics policy platform are the “troika” for urban logistics development. Logistics infrastructure equipment is the primary physical condition for organizing the operation of the logistics system. For urban systems, the spatial layout of urban logistics facilities and outlets directly affects the efficiency of urban logistics, urban transportation, and people’s livelihood. The information network platform of urban logistics mainly refers to various information systems closely related to the operation of the urban logistics system. It is pretty critical to the overall optimization of the urban logistics system. The sound development of urban logistics requires the joint efforts of the government and the market. In addition to complete facilities and information platforms, the development of urban logistics also requires a sound policy environment as support.

3.1.2 Reliability of Urban Logistics System 3.1.2.1

Meaning

The reliability of the urban logistics system is based on urban development planning, changes in urban logistics demand, population distribution and other factors. It is market-oriented and enterprise-based with urban logistics services as the support and taking into account the effects of urban logistics on economy, environment and people’s livelihood. The reasonable choice of transportation methods, adjustment of urban road network planning, and coordination of various units in the system facilitate the reasonable allocation of urban logistics resources and increase the probability of the city completing its logistics functions. Research on the reliability of urban logistics system should be based on the overall urban planning, market laws, and urban resources. In recent years, cities in China are entering a stage of rapid development. Each city has formulated an overall plan according to its characteristics. The urban logistics system is an integral part of the overall urban planning. The urban logistics system should be coordinated with the overall urban planning for mutual promotion and integrated development. It is necessary to consider the future development needs of the city, and base itself on the current market demand. If large scale, new technology, and versatile functions are blindly pursued, but yet the current economic development of the city and the future development trend ignored, the logistics system will be out of sync with the city’s needs and its economic development hindered. The reliability of the urban logistics system can be analyzed from the perspective of urban distribution network structure. The reliability of the distribution network is composed of that of the nodes and that of the arc connecting nodes. The reliability of the entire distribution network can be obtained by calculating the reliability of the

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3 Meaning of the Reliability of Urban Logistics System

nodes and arcs. Assuming that a node i and the arc connecting it with the next node i → j are a basic unit r , the reliability of the node is ri , the reliability of the arc i → j is ri→ j , then the reliability of this basic unit can be expressed as: Ri j = ri · ri→ j

(3.1)

Assuming that the value of any basic unit xi j of the network is 1 when it is connected and 0 when not, that is, the connection probability of the basic unit P is Ri j at xi j = 1 and 1 − Ri j at xi j = 0, the expected smoothness of the basic unit of the network is E xi j = Ri j . Assuming that the distribution network composed of basic units has m paths from the node at the start to that at the  end, then [i, j) ∈ Pm , the reliability of any one of the paths can be expressed as [i, j) xi j , and the reliability of the entire network as a path can be expressed as: ⎧ ⎨

R= E 1− ⎩

3.1.2.2

m  m=1

⎡ ⎣1 −

 [i, j)∈Pm

⎤⎫ ⎡ ⎤ m ⎬   ⎣1 − xi j ⎦ = 1 − Ri j ⎦ ⎭ m=1

(3.2)

[i, j)∈Pm

Function

With the rapid development of the economy and society, commodities and customers are becoming more diversified, which has led to more uncertainties in the modern economic system. The logistics system serves as a bridge between production and consumption. The degree of reliability has a significant impact on the smooth operation of enterprises, economy, and even the society. The reliability of urban logistics reflects not only the ability of the various components of the logistics system to connect and coordinate for each other, but also whether the urban logistics can stably and continuously ensure and adapt to the rapid development of urban economy and society. (1)

Improve business efficiency. Urban logistics can effectively serve business flows. Except for futures transactions with non-physical delivery, general business flows must be accompanied by corresponding logistics processes. In this entire circulation process, logistics is actually the successor and service provider of business flows. For example, e-commerce must rely on logistics or will be rendered an empty talk. Urban logistics can guarantee production. From the purchase of raw materials to the production process, and then to the sale of products, logistics is necessary. As far as the entire production process is concerned, it is a series of logistics activities. Rationalized logistics can reduce costs by reducing transportation costs, reduce capital pressure by optimizing inventory structure, improve efficiency by strengthening management, and thus realize corporate profits. A reliable urban logistics system relies on its nodes and line network system, which can reduce transaction costs in

3.1 The Concept of Reliability of Urban Logistics System

(2)

(3)

57

economic activities and improve the efficiency and level of operation for businesses with its overall function. It helps minimize transaction costs, improve logistics operation efficiency, and enhance the competitiveness of enterprises. Promote urban economic development. The logistics industry is a new growth point for urban economy. The modern logistics industry can propel the development of multiple industries and sectors such as transportation, storage, packaging, loading and unloading, circulation, processing, and related information activities. Logistics is also conducive to the optimization of regional industrial structures. The logistics industry is a composite service one with a wide range of fields. It plays an important role in promoting industrial structure adjustment, driving forward the development of industrial clusters, and realizing the optimization of industrial structure. The logistics industry helps enhance the comprehensive competitiveness of a city, which is mainly manifested in the combined benefits of productivity and circulation. The logistics industry is one of the elements of the circulation. The logistics industry helps improve urban infrastructure. The development of urban logistics, the improvement of the construction of the transportation network, and the enhancement of the level of urban infrastructure contribute to the sustainable development of the city. The reliability of the urban logistics system directly affects the smooth operation of urban economy. A reliable urban logistics system helps the city to attract foreign investment, cultivate regional economy with it at the center, make the layout of its economic structure more reasonable, enhance its competitiveness, and promote the development of its entire economy. Therefore, to develop an optimization mechanism for the urban logistics system with reliability as the core is conducive to maintaining the stability of social and economic development. Maintain the stable operation of society. In fact, every aspect of life involves logistics. It meets the residents’ needs for daily necessities through transportation and distribution and enables the residents to buy fresh fruit, vegetable, meat, and egg products by dint of advanced storage technology. With the thoughtful service of the moving company, people find it easy to move into their new homes. Distribution in urban logistics is a livelihood project related to social stability. Urban production and daily necessities require a reliable and effective urban logistics system as a guarantee. If the supply of products for production and daily life cannot be guaranteed, it will not only affect the basic lives of the people but will also have a huge impact on social security and stability and cause losses on a more extensive scale.

The sustained economic development can gradually bring together various factors of production such as customer flow, business flow, and capital flow. However, the ultimate goal is to spread the goods. If there is not a developed degree of circulation and commercial trade as the guarantee, a large number of products will lie in disuse, which will affect the realization of commodity value and use value, and interrupt the economic operation. Therefore, during economic development, a reasonably developed logistics system plays a fundamental role. As an essential part of the urban financial system, the urban logistics system guarantees the city’s economic security

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and social security. How to build a reliable urban logistics system to provide security assurance for urban production and life in the event of emergencies is of great significance.

3.2 Components of the Reliability of Urban Logistics System A logistics network has two components, namely points and lines. Points, also known as nodes are places where a large quantity of logistics infrastructure are brought together, including factories, distribution centers, stations, and terminals. Lines are the routes connecting the nodes, and in broad sense refer to all land, water, and air routes that can be used and travelled in, and in narrow sense only the routes and courses that have been opened and can be used for logistics operation following regulations. Points and lines, through organic connection, have formed a logistics network. The reliability of the logistics system can be divided based on the elements of the logistics network, that is, the reliability of the nodes and the reliability of the lines.

3.2.1 Node Reliability The logistics nodes refer to the nodules connecting the logistics lines in the logistics network, and all those for material transfer, collection, distribution, and storage and transportation, such as ports, airports, train freight stations, highway hubs, large public warehouses, modern logistics (distribution) centers, and logistics parks. Node reliability refers to the ability of a node to provide a certain amount of goods within a specified period of time. The operation of the logistics node is complicated, and often requires warehousing, packaging, loading and unloading, and handling of goods. Therefore, the reliability of the logistics node can be determined by that of the storage subsystem, the packaging subsystem, and the loading and unloading subsystem. As shown in Fig. 3.3, the factors that influence these subsystems include the technical equipment, personnel operating, external factors, and customer demand changes.

3.2.1.1 (1)

Technical Equipment Factors

Facilities and equipment. Facilities and equipment are essential factors of logistics nodes, including those used during logistics packaging, warehousing, loading, and unloading. They are used to complete multiple tasks in all aspects of logistics operation and occupy a significant position in logistics nodes. The logistics center is a very large logistics node with multiple logistics facilities,

3.2 Components of the Reliability of Urban Logistics System

59

comprehensive functions and basic functions brought together in an intensive way. It is a concentrated and large-scale logistics facility and the intersection of multiple logistics routes. It has comprehensive functions, intensive functions, transshipment functions, centralized inventory functions, command functions, and adjustment and optimization functions. An unreasonable location of the logistics center or unreasonable planning of facilities within will seriously affect the realization of the functions of the entire logistics center and even the urban logistics system. External transportation facilities, such as ports, airports, and railways, are basic ones to connect a city with logistics beyond it. The failure of these transportation facilities will affect the operation of entire logistics. With the reconstruction and expansion of the city, many of the storage facilities in it have been demolished, and the logistics facilities are moved farther away from the city center. Due to the low profit rate and long term of returns on investment in the logistics industry, logistics enterprises find it hard to settle in most cities and logistics facilities become less. Logistics facilities and equipment can ensure the efficient, high-quality, and lowcost operation of the entire logistics process. The operating efficiency of the logistics nodes without the logistics equipment will be extremely low or even rendered zero. Although advanced logistics facilities and equipment have an essential impact on the reliability of urban logistics, to choose facilities and equipment suitable for the enterprise is equally important to the reliability of urban logistics. Being advanced means that the performance of logistics equipment is getting more developed and automated, as embodied in higher speed, higher accuracy, and better stability. With the emergence of the contradiction between the expansion of warehouses and rapid customer response, logistics enterprises need to pick and distribute goods in an extremely short period of time and continuously improve the running speed and processing capacity of the new logistics forces. Therefore, the logistics equipment such as picking system

Fig. 3.3 Factors affecting node reliability

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3 Meaning of the Reliability of Urban Logistics System

and conveyor system should be developed toward a higher-speed goal. Based on the planning for and selection of logistics equipment, it is necessary to select equipment suitable for the enterprise and achieve the goal with minimum investment. For example, Wal-Mart (China) Logistics Center, BIG-W (Australia) Logistics Center, Lianhua (Shanghai) Logistics Center, 7–11 (Taiwan) Logistics Center, and other such companies have very different facilities and equipment. The key of modern logistics equipment lies in suitability instead of investment or advanced technology. (2)

Logistics technology. Logistics technology refers to circulation technology or material transportation technology, and that to transfer and store the produced materials to provide intangible services for the society. Logistics technology is closely related to the entire process of actual logistics activities. The level of logistics technology is directly related to the improvement and effective realization of logistics activities. Logistics technology includes the basic technical methods such as logistics forecasting, inventory control, and logistics packaging. The logistics industry witnesses changes with each passing day, and new logistics technology and concepts play a vital role in its development, such as bar code technology, EDI technology, radio frequency technology, GIS technology, and GPS technology. They have extensively promoted the development of the logistics industry. If the logistics technology cannot be applied properly, logistics service capacity of the node will be rendered insufficient. For example, with the rapid development of e-commerce in recent years, its related industries have also greeted tremendous growth, especially the most closely related logistics industry. It has seen quick development in terms of business volume and market size. But in the course of development, delivery industry might sometimes work at an overload, which indicated the limited processing capacity of express companies as more orders were placed than goods delivered in a short period of time. A large number of packages would be piled up in the warehouses and the delivery delayed or even related business broken down. It was partly because that delivery is dependent on labor too much, and the driving force for technological upgrading insufficient. At this stage, the delivery industry in China is still a labor-intensive one with no advanced logistics technology applied to the logistics operation [4].

The effective use of logistics technology helps to ensure the reliability of urban logistics nodes. For example, the application of RFID technology to the cold chain logistics for vegetable can effectively ensure that the source information of raw materials and various ingredients is recorded on the production line and the products are placed under strict supervision, control and management in each step of the processing. In terms of transportation, real-time monitoring of the temperature and other related information about vegetable products can be realized, for more intelligent, efficient and accurate distribution. In terms of sales, the shelf life can be monitored, and realtime replenishment made. RFID can render each link of the cold chain logistics for vegetable more efficient and the entire system more reliable. (3)

Information system. The flow of information also accompanies that of goods, and information plays a vital role in the nodes of the urban logistics system.

3.2 Components of the Reliability of Urban Logistics System

61

Logistics information plays an important role in the establishment of strategic plans for logistics, and also in the planning stage for such operation as budgeting, ordering, inventory control, loading and unloading, packaging, and distribution, and in determining the layout of logistics nodes, replenishment strategies, and planning for transportation routes. If the logistics information is inaccurate and untimely, it will cause shortages. The lack of raw materials will force enterprises to fail in production, and the shortage of daily necessities will render the residents’ daily needs unsatisfied. The quality of the goods cannot be effectively guaranteed and customer satisfaction reduced due to information delay in the transportation process. For the entire logistics decision-making, the lack of information will cause errors on the whole. The flow of urban logistics information directly affects the operation of enterprises. The low reliability of logistics information will affect the activities of industry and commerce in cities. For example, when transporting goods, companies must be fully informed of the demand, transportation volume, weather, and others to organize transportation. When loading, unloading, and handling, enterprises must be informed of the cargo space and shipment. Otherwise it may cause repeated handling and affect the quality of the cargo. Before storing goods, enterprises must master the current inventory quantity, specifications, and other relevant information to reasonably allocate the cargo space, improve the utilization rate of the warehouse, and provide convenience for delivery. If a unified platform in terms of logistics information is absent for nodes, the information communication between various subsystems will be very difficult, which will lead to errors or delays in information or information delays, difficulty of information transfer between various nodes, and adverse impact on the reliability of the entire system.

3.2.1.2

Personnel Operational Factors

The quality of employees cannot be separated from the reliability of urban logistics. Human capital determines the extent to which the logistics industry in China can be developed. Therefore, the quality of employees is crucial to the development of the logistics industry [5]. (1)

(2)

Packaging process. Packaging is a primary task that brings convenience to the storage, transportation, adjustment, and sales in the circulation, such as loading and unloading, inventory, and palletizing. It also functions to protect products from sun, rain, dust, and other kinds of pollution and attacks. Packaging is a vital link that determines the success of distribution, and also one of the critical factors that affect the reliability of urban logistics nodes. Improper packaging or insufficient packaging by personnel will cause inconvenience to storage and delay the delivery of goods. Storage process. Storage is a guarantee to meet customer needs and respond to emergencies and an essential part to ensure the reliability of urban logistics and

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(3)

3 Meaning of the Reliability of Urban Logistics System

distribution system. Storage should be carried out in a planned and periodic manner based on experience and customer orders. If the storage volume is too large, it will increase the storage cost. A large amount of storage will extend the capital operation cycle and bring certain risks to products with frequent updates. To inspect the goods into and out of warehouses, make an inventory regularly, and check the quality and quantity of the goods directly affect the reliability of the nodes. Loading and unloading process. Loading, unloading, and handling play a cohesive role in the entire logistics process and directly affect subsequent operation. According to statistics, the unloading time when the transporting distance by railway is less than 500 km will exceed the actual transportation time. The factory needs to load and unload 252 tons for every 1t of finished products produced, and spends 15.5% of the processing cost [6]. Therefore, improving the efficiency of loading and unloading directly helps improve logistics efficiency and reduce costs [7]. The basic actions of loading and unloading include loading, unloading, stacking, warehousing, delivery from storage, and shortdistance transportation between these procedures, and are necessary along with transportation and storage activities.

Loading, unloading, and handling will affect the quality and speed of other logistics activities. If loading and unloading are done violently against the regulations, the packaging of the goods and the goods themselves will tend to be damaged. Too long a proportion of the loading and unloading duration will affect the speed of receiving and sending goods, and hence the turnaround speed. Due to the low quality and poor operational skill of personnel engaged in loading, unloading, and handling, these activities cannot be done in a centralized manner with the difficulty of employing mechanical equipment on a large scale. It will greatly increase the difficulty of loading, unloading, and handling, prolong the operating time, and leads to inefficiency. Because loading, unloading, and handling activities are trivial and difficult for systematic analysis, and enterprises tend to consider not in a systematic manner and ignore the unloading issue and the convenience of delivery from storage, all steps cannot be done cohesively with low execution efficiency. Randomness and invalid activities are often seen, thereby reducing the logistics speed, and causing more losses of goods. According to statistics, the probability of cargo damage during loading, unloading, and handling is higher than that during transportation and warehousing. Excessive loading, unloading, and handling activities will significantly increase the likelihood of cargo damage [7]. China has a large population and the development of its logistics industry relies on cheap labor. Employees, especially front-line personnel, see a poor academic background and few front-line personnel have technical competence. The couriers in the logistics industry have the most direct contacts with consumers. Many of them now still sort out packages without sufficient care, which will adversely affect the quality of goods and create a negative image of the company. A study conducted jointly by sohu.com and Legal Daily showed that customers were dissatisfied with the services provided by express companies. According to the survey of satisfaction with

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the services of express companies, 29.76% were “dissatisfied” because for 8.61% “goods were lost due to the fault of the express company”, and for 13.25% “the couriers were rude”. Employees in the express industry have the most direct contacts with consumers, and their awareness of and attitude towards service directly affect customer satisfaction and service reliability. Besides, they will fail to adapt to the new development needs with low professional quality and awareness of service. The lack of talents with professional and management skills in logistics has led to the limitation of the application of advanced software and hardware logistics technology, and that of the logistics industry. Many logistics companies have fallen into the “low-tech trap”. They would rather hire more cheap workers and use equipment not advanced for low production costs than invest in technological updates and improvement of workers’ quality. These logistics enterprises are not very motivated for technological progress and low in independent innovation capabilities. The contradiction may not be seen for now. As the labor supply decreases and the demographic dividend disappears in China, the high labor costs in the future may become a burden on enterprises. In recent years, voice-activated sorting and light-controlled sorting have been applied to a certain extent, and automated storage systems developed to a certain extent. The wide application of high technology in the logistics industry is the future trend. Therefore, the current situation of China’s logistics industry will hinder its sustainable development. Enterprises should be forward-looking for their development and see the future development direction of the logistics industry; otherwise, they may be eliminated. The quality of logistics personnel and operating factors have impeded the development of the logistics industry in China. The upgrading of the logistics industry structure is slower than that of the industrial structure and consumption structure, for which the industry can hardly meet the higher demand for logistics and the reliability of the entire logistics system will be weakened.

3.2.1.3 (1)

External Factors

Policy factors. Policies in China are of great significance to the development of logistics, and their changes will significantly impact the development of the logistics industry. The government functions in the development of urban logistics in China are far from being well developed with such problems as insufficient government’s logistics management system, insufficient government’s guidance on modern logistics, and inadequate government’s regulation and supervision of the logistics market. The development of urban logistics requires the urban infrastructure system to be built, the logistics enterprise networks improved, the logistics systems and standardization constructed, and a suitable environment for the development of logistics enterprises created. The unpredictability of the policy brings a series of uncertainties and will directly affect the reliability of the urban logistics system.

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In 2010, the National Development and Reform Commission formulated the Cold Chain Logistics Development Plan for Agricultural Products (hereinafter referred to as the Plan). The Plan proposes to enhance the cold chain logistics level for meat and aquatic products, and strengthen the ability to ensure food safety. The Plan clarifies the seven main tasks for the development of cold chain logistics for agricultural products: First, it is necessary to popularize the concept and technology of modern cold chain logistics. Second, it is necessary to improve the cold chain logistics standard system. Third, it is necessary to establish a cold chain logistics system for wide varieties and key areas of agricultural products. Fourth, it is necessary is to accelerate the cultivation of third-party cold chain logistics enterprises. Fifth, it is necessary to strengthen the construction of cold chain logistics infrastructure. Sixth, it is necessary to accelerate the upgrading of cold chain logistics equipment and technology. Seventh, it is necessary to promote the information technology of cold chain logistics. The Plan deals with logistics facilities and equipment, technology, information platform, and others, and will help promote the further construction of the urban cold chain logistics system. In 2011, the General Office of the State Council issued the Opinions on Policies and Measures to Promote the Sound Development of the Logistics Industry (hereinafter referred to as the Opinions). The Opinions states that: it is necessary to effectively reduce the tax burden on logistics enterprises, increase land policy support for the logistics industry, facilitate the passage of logistics vehicles, accelerate the reform of the logistics management system, encourage the integration of logistics facilities resources, and promote logistics technology innovation and application, increase investment in the logistics industry. In 2011, the General Office of the State Council issued the Opinions on Strengthening the Construction of the Circulation System of Fresh Agricultural Products, which clarifies that it is necessary to focus on strengthening the link between production and marketing, and the construction of infrastructure for the circulation of fresh agricultural products, create a circulation model for fresh agricutural products, increase the degree of organization for circulation, improve circulation chains and market layout, further reduce circulation links, lower circulation costs, establish an efficient, unblocked, safe, and orderly circulation system for fresh agricultural products, and achieve the goal of stabilizing market supply and price of fresh agricultural products. In 2014, the State Council issued the Medium and Long-term Plan for the Development of the Logistics Industry (2014–2020). Based on the summary of the current development and situation of the logistics industry in China, it proposes three major development priorities, seven major tasks, twelve key projects, and nine major safeguard measures, to accelerate the development of China’s modern logistics industry and promote the sound development of the logistics industry. To accelerate the development of the logistics industry is the key direction of advancing the supply-side structural reform and increasing the supply of public products and public services to solve the longstanding prominent problems of high cost and low efficiency that have long existed in the logistics field [8]. The Special Action Plan for Cost Reduction and Efficiency Increase in the Logistics Industry (2016–2018) issued by the National Development and Reform Commission in 2016 points out four key actions: streamlining administration and delegating power to establish a more fair and open new market order;

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reducing taxes and fees to foster the new functions of enterprises for innovation and development; making up for shortcomings and strengthening the basis to improve facilities and standard systems that support the efficient operation of logistics; interconnectivity to establish new mechanisms for collaboration, sharing, and security. In addition, there are five safeguard measures: increase investment as support for the construction of important logistics infrastructure; improve and implement the land use policies that support the development of the logistics industry; broaden the investment and financing channels for logistics enterprises; give full play to the role of industry associations; strengthen organization, coordination and supervision and inspection [9]. In 2018, the General Office of the State Council issued the Opinions on Promoting the Coordinated Development of E-commerce and Express Logistics related to six areas to improve the coordinated development of e-commerce and express logistics: First, it is necessary to strengthen system innovation and optimize the policy and regulatory environment for coordinated development. Second, it is necessary to strengthen planning and guidance to improve the e-commerce express logistics infrastructure. Third, it is necessary to strengthen standardized operation and optimize the management of e-commerce distribution. Fourth, it is necessary to strengthen service innovation and improve terminal service capabilities in the express industry. Fifth, it is necessary to strengthen standardization and intelligence to improve the efficiency of joint operation. Sixth, it is necessary to strengthen the green concept and develop the green ecological chain [10]. The government’s logistics promotion policies include those for the construction of facilities and equipment, the promotion of advanced technology, and the construction of logistics information platforms. These policies have promoted the development of urban logistics to a certain extent. However, some with hysteresis cannot adapt to the current development of urban logistics in speed. (2)

Emergencies. Emergencies refer to those that occur suddenly and cause or may cause heavy casualties, property losses, damage to ecological environment, and severe harm to public safety. For example, the surge in consumption during the Spring Festival and other holidays has greatly impacted the commercial logistics and distribution system. The spread of radiation in Japan caused panic purchase of daily necessities, including salt, among citizens in Beijing. The outbreak has driven high the demand for drugs and supplies for COVID-19 prevention and treatment. Such urgent demands that saw their peak within a short term have brought enormous challenges to the reliability of the urban logistics system. How to organize distribution in a short period of time and ensure supply is an urgent problem to be solved. It is related to the stability of the logistics system and also people’s livelihood and even social stability. Public emergencies occur suddenly with high complexity, destructiveness, and continuity. Many sudden natural disasters will cause the interruption of logistics and the failure of logistics nodes. Therefore, emergencies are also an essential factor with uncertainty in the urban logistics distribution system.

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3 Meaning of the Reliability of Urban Logistics System

Customer Demand Changes

Logistics is a bridge between production and the market. The demand is sensitive and changeable, so the logistics system shows variability and uncertainty [11]. In March 2009, Truck Logistics and Beijing Daolue Consulting Co., Ltd. jointly conducted a survey on the changes in the needs of logistics companies for logistics. A total of 100 logistics companies and transportation companies across China were investigated. The survey results are shown in Fig. 3.4. Changes in demand impact on almost all logistics companies, and changes in logistics demand impact on logistics companies’ inventory strategies and transportation strategies. Changes in logistics demand include customers’ higher requirements for product quality in transit and higher uncertainty in the quantity of goods needed, and have thus put forward higher needs for the flexibility and reliability of logistics. For example, with the development of cold chain logistics, advanced refrigeration and preservation technology is needed to ensure the quality of goods in transit. When customer demand suddenly increases or decreases, how to organize distribution to meet demand and reduce costs is considered. As shown in Fig. 3.5, changes in logistics demand directly lead to a decline in logistics enterprises’ transportation growth rate. For logistics enterprises, the growth of transportation is a vital indicator. Freight vehicles, as the essential equipment for logistics transportation, require higher cost performance and technological upgrades due to changes in demand, and need to be up to more flexible and demanding transportation requirements. Changes in demand also directly impact the operation mode and service quality of logistics service providers. If logistics companies do not improve their flexibility and reliability, they may fail to satisfy the urban needs in the event of changes in demand. Fig. 3.4 The impact of changes in logistics demand on logistics companies. Source Wu Qingyi [12]

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Fig. 3.5 The impact of changes in logistics demand on logistics companies. Source Wu Qingyi [12]

With demand identified, logistics companies will find out about the needs of customers in advance and reasonably ensure the inventory level to organize transportation. However, with the continuous development of urban economy, the needs of urban residents have become more diverse and random, which puts forward higher requirements for the logistics companies in terms of distribution. From the perspective of customer demand, frequent changes to orders and irregularities in order will mainly affect demand forecasts and lead to corresponding changes in warehousing and scheduling. The uncertainty of demand mainly comes from the incorrectness of forecasting methods, decision-making errors, and the volatility of customer demand. E-commerce, a new type of trade method, enables people to get rid of geographical restrictions. Customers can complete the previously complicated commercial activities in a convenient way. With the help of the Internet technology, the time and space between production and consumption have been reduced. People are free from the many restrictions of traditional way of shopping, which has caused some new changes in consumer behavior as different from conventional behavior patterns. (1) A purchase mode that is also fun. In the face of great pressure in the city today, consumers do not have sufficient time to visit physical stores. However, they have high demands and hope to relieve the stress of life and work through shopping and satisfy their curiosity. Some e-commerce companies can meet their needs by allowing them to enjoy the fun of shopping without leaving home. (2) A personalized consumption model. As people’s living standards are improved, they have experienced changes in their consumption concepts and valued product differentiation and customization more during the purchase. These are the changing factors of customer demand. If the law intrinsic to demand changes cannot be found, logistics warehousing, transportation, and circulation and processing, and the reliability of the system will be affected [13].

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3.2.2 Route Reliability The route is the channel forming the logistics network and connecting the scattered logistics nodes. Route reliability can be divided into connectivity reliability, temporal reliability and capacity reliability.

3.2.2.1

Connectivity Reliability

Connectivity reliability refers to the probability of maintaining connectivity between nodes in the network. Asakura defines reliability as: Rp =

n 

r j y j (1 − r j )1−y j (y)

(3.3)

j=1

where R p represents the connectivity reliability between the road network and path p; y j represents the state of road section j, represented by a variable between 0 and 1, and road section j is 1 if connected and 0 if not; r j represents the probability that the road section j is unblocked, and y is the state variable vector.  (y) = (y1 , y2 , . . . , yn ) =

1, P I (y) ≤ c 0, P I (y) > c

(3.4)

If the performance index P I (y) is smaller than or equal to the given threshold c, the system state (y) is 1, otherwise it is 0. Connectivity reliability reflects whether the road unit is connected or not, and indicates the probability that the road network is in a critical state. It is suitable for evaluating the research on road network under extreme conditions caused by disasters.

3.2.2.2

Temporal Reliability

Temporal reliability is the probability that a vehicle will go from the starting point to the end point within a specified period of time. It is an indicator for evaluating the stability of travel time and is suitable for the evaluation of daily operation of the road network. Belletal proposed the use of sensitivity analysis to calculate temporal reliability, with the formula as follows: Min[h T (ln(h) + αg T h]

(3.5)

s.t. m(1 − δ) ≤ Ah ≤ m(1 + δ)

(3.6)

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t (1 − δ) ≤ Bh ≤ t (1 + δ)

(3.7)

h≥0

(3.8)

h represents the path flow, g the corresponding path cost, A the path dependent variable matrix, B the start and end points and path dependent variable matrix, t the traffic flow between the start and end points, m the detected path flow, and δ the tolerance coefficient. Formula (3.6) indicates that the difference between the estimated flow of the road section and the measured flow is within the specified range, and Formula (3.7) indicates that the estimated flow of the path is consistent with the flow at the start and end points. The travel time is obtained with the formula. Assuming that it follows the normal distribution, the temporal reliability can be obtained according to Formula (3.9). R j = Pr (g j ≤ g oj ) = (

g oj − μ δ

)

(3.9)

where R j represents the temporal reliability of path j; g j represents the travel time on route j; and g oj represents the prescribed travel time. 3.2.2.3

Capacity Reliability

Anthony Chen and others proposed the concept of capacity reliability. Capacity reliability refers to the probability that the road network’s capacity can meet a certain level of traffic demand at a certain service level. First, the maximum capacity of the road network should be clarified, that is, the storage capacity coefficient of the road network: after the OD matrix of a known demand is enlarged several times, it will neither be greater than the capacity of the road section when it is allocated to the road network, nor will it exceed the specified ratio of flow to capacity. The largest multiple is called the road network storage capacity coefficient. A two-level goal planning model is established: Maxμ

(3.10)

s.t. va (μq) ≤ ca

(3.11)

where va (μq) indicates the balanced flow on section a; ca indicates the traffic capacity of road section a; μ indicates the storage capacity coefficient of the road network; and q represents the OD demand matrix.

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Minz = s.t.

 r ∈R

va =

 a∈A

va

ta (x)d x

fr = μqw ∀w ∈ W 

(3.12)

0

fr δar ∀a ∈ A

(3.13) (3.14)

r ∈R

fr ≥ 0 ∀r ∈ R

(3.15)

where W represents all paths in the road network; Rw represents all paths between OD and w; Z represents the user balance objective function; va indicates the traffic flow on the road section a; ta (va ) indicates the travel time on the road section a; qw indicates the actual traffic demand between OD and w; fr represents the traffic flow on the path r; δar represents a variable between 0 and 1, if road section a is on the path r , then δar = 1, otherwise δar = 0. This coefficient can be used to calculate the reliability of the capacity of the road network, and represents the evaluation of the degree to which the existing road network satisfies the expected traffic demand D, R(D) = P(μ ≥ D). R(D) indicates the reliability of the road network when the demand is D. The upper and lower limits of the road network capacity reliability are determined by the expected traffic demand. When D = 0, R(D) = 1; when D = ∞, R(D) = 0, that is, there is no traffic demand, and the road network is 100% reliable. When the demand is infinitely great, the reliability of the road network is 0. Capacity reliability is used to evaluate the probability that the road network’s capacity meets a certain level of demand as traffic conditions are deteriorated. It indicates whether the current road network level can meet the expected demand. The calculation of the capacity of the road section on which the capacity storage coefficient depends requires a large amount of actual data, which can hardly be obtained. The capacity reliability describes the driver’s route choice behavior assuming that the driver has a comprehensive understanding of the road conditions, and is not suitable for analyzing the driver’s route choice behavior in case of traffic contingencies.

3.2.3 Network Reliability The logistics nodes that perform various logistics functions in the urban logistics system and the logistics routes that connect the scattered logistics nodes constitute the logistics network of the urban logistics system.

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3.2.3.1

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Classification of Nodes in the Logistics Network

The logistics node is an indispensable part of the logistics system and an important place where various logistics functions are exerted. With the development of modern logistics, logistics nodes have become differentiated, but still have similarities and differences, with significant boundaries. The nodes have not taken shape, so their function, utility, structure, and techniques are developed with the pace of modern logistics. Therefore, scholars have no clear conclusions about the classification of logistics nodes. This book will try to classify the logistics nodes based on their main functions. The schematic diagram of logistics nodes is shown in Fig. 3.6. (1)

(2)

(3)

Transit node. The transportation of goods cannot depend on one mode alone sometimes. There are transfers of modes during the transportation that require the presence of transit nodes. They help increase the efficiency of the previously time-consuming and laborious transfers. For example, marshaling yards in railway transportation, wharves and ports in waterway transportation, and airports in air transportation all fall under such a category. Storage node. The logistics nodes in the logistics system that mainly function to store goods can effectively improve the flexibility of production and the ability to deal with emergencies for enterprises. In the logistics system, all types of warehouses are such nodes. With the continuous development of modern logistics, storage nodes have shown new external manifestations. For example, front-loading warehouse is one of the new trends for storage nodes. Circulation node. It refers to the logistics node that organizes and assists the movement of goods in the logistics system. With the development of modern logistics, such nodes mainly exist in the form of circulation and distribution centers.

Fig. 3.6 Schematic diagram of logistics nodes

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In real life, a logistics node can have the functions of two or more logistics nodes, for meeting the actual needs for daily operation. For example, a small warehouse is usually set up in a circulation node, so the node can function for storage. In this case, the nodes are generally classified according to their primary functions. If the functions are not independent but work together to complete the task, the node is a comprehensive one. (4)

Comprehensive node. It refers to the logistics node with two or more functions which are not independent in daily operation but work together to complete tasks together. This kind of node boasts well-equipped facilities and equipment, and coordinated and efficient technological process, and can basically meet various needs in logistics services independently. It is an intensive node that integrates multiple functions. Such a kind stems from the increasingly large volume and complexity of modern logistics. To improve the accuracy of logistics and make the logistics system more simplified and more efficient, multiple functions should be integrated in one node, which marks a development trend for logistics nodes in modern logistics.

3.2.3.2

Classification of Routes in the Logistics Network

The logistics route connects various logistics nodes in the logistics system. It is the path for the movement of materials in the logistics system and the line for the transportation of goods. This book classifies logistics routes according to different modes of transportation. (1)

Railway transportation. Railway transportation is the main artery of the national economy and one of the most important modern modes of transportation in China. Railway transportation is connected with highways and waterways to form a transportation network extending in all directions, and necessary for the realization of commodity circulation [14]. According to data released by the National Bureau of Statistics, the total operating mileage of railways in China had been 131,000 km as of the end of 2018. Its freight volume had achieved 4.03 billion tons and the freight turnover volume 2882.0 billion tons per kilometer, which respectively accounted for 7.8 and 14.1% of the total across China. During the same period, the railways delivered 3.37 billion passengers and saw a turnover of 1414.7 billion passengers per kilometer, accounting for 18.8 and 41.3% of the total.

The railway network in China comprises eight main railway lines with “five vertical ones and three horizontal ones”. Those connecting the main railway lines are known as hub stations, mainly including Beijing, Zhengzhou, Chengdu, Xuzhou, and Lanzhou. “The five vertical lines” refer to those in the north–south direction: Beijing-Harbin Railway, Beijing-Guangzhou Railway (Beijing-Harbin Railway passes through Beijing, Hebei, Tianjin, Liaoning, Jilin, Heilongjiang and other areas and cities, and Beijing-Guangzhou Railway through Beijing, Hebei, Henan, Hubei, Hunan, Guangdong and other areas and cities); Beijing-Shanghai

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Railway that passes through Beijing, Hebei, Tianjin, Shandong, Anhui, Jiangsu, Shanghai and other provinces and cities; Beijing-Kowloon Railway that runs through Beijing, Tianjin, Hebei, Shandong, Henan, Anhui, Hubei, Jiangxi and Guangdong; Taiyuan-Jiaozuo-Zhicheng-Liuzhou Railway passes through Shanxi, Henan, Hubei, Hunan, and Guangxi, and connects Datong-Puzhou Railway, Beijing-Baotou Railway, Shijiazhuang-Taiyuan Railway, Beijing-Guangzhou Railway, GansuHaizhou Railway, Hunan-Guizhou Railway, and Guizhou-Guangxi Raiway with the Yangtze River system, and is another major traffic artery that runs parallel to the Beijing-Guangzhou Railway across central China composed of TaiyuanJiaozuo Railway, Jiaozuo-Zhicheng Railway and Zhicheng-Liuzhou Railway. Baoji(Chengdu)-Kunming Railway runs from Baoji in the west section of Gansu-Haizhou Railway to Kunming in the south through provinces and cities such as Shaanxi, Sichuan, Guizhou, and Guangxi [14]. “The three horizontal lines” refer to three main railway lines in the east–west direction: Lianyungang-Alataw Pass Railway that passes through the five provinces of Jiangsu, Henan, Shaanxi, Gansu, and Xijiang, and is composed of Gansu-Haizhou Railway, Lanzhou-Xinjiang Railway, and North Xinjiang Railway; Beijing-Baotou-Lanzhou Railway that consists of Beijing-Baotou Railway and Baotou-Lanzhou Railway. Beijing-Baotou Railway is a line for coal transportation from Shanxi and a part of the international line connecting Mongolia and Russia. Baotou-Lanzhou Railway runs from Baotou in Inner Mongolia to Lanzhou in Gansu; Shanghai-Kunming Railway that passes through the six provinces of Zhejiang, Jiangxi, Hunan, Guizhou, and Yunnan, and is composed of Shanghai-Hangzhou Railway, Zhejiang-Jiangxi Railway, Hunan-Guizhou Railway, and Guiyang-Kunming Railway. Let’s take a look at the history and current situation of railway development abroad. The railroad in the US started to see development in the 1830s. The first railroad, Baltimore-Ohio Railroad, was built and put into operation in 1830. Its large-scale construction was completed in the 1920s. The total railway mileage in the US reached about 410,000 km, its peak, in 1916. Then for the purpose of developing highways and aviation industry and restricting railway monopoly, the US government has intervened in the railway market with various means, which caused a rapid decline in its scale. A large number of railway lines were forced to be closed and demolished. The length of the road network has been reduced. It was not stopped until the US government adopted a series of measures to relax the railway control, such as the famous Staggers Rail Act to effectively promote the recovery of the railway market at that time [15]. Despite the suspension, the United States is still far ahead of others in terms of the total railway mileage. The US freight rail network mainly extends in the east–west direction, with few lines in the north–south direction. There are bottleneck sections in Chicago, southern California, New Orleans, Texas, Cincinnati and the east coast. Now the rail network in the United States with congestion is less than 1% of the total. Still, with the increasing demand for transportation, the disadvantages from the insufficient road networks will become increasingly prominent [16]. When it comes to German railways, the “Netz 21” program has to be mentioned for it has had an essential impact on the construction of modern infrastructure and the upgrading of communication equipment. In the early twentieth century, the

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German railway network roughly took shape, but after the Second World War, it has been reduced on a large scale. According to “Netz 21”, the new road network will be classified according to its role and the level of infrastructure into three types: priority network, consisting of the long-distance high-speed passenger transportation network, freight network, and urban express railway; capacity network mainly of the mixed lines for long-distance, short-distance and freight transportation; regional network as a supplement for the first two [17]. There are many other famous examples of passenger transportation by railway abroad. For example, Shinkansen in Japan, a high-speed railway system with the equal famous reputation to that of TGV in France, ICE in Germany and CRH in China, is the pioneer of high-speed railway system and has provided valuable experience for the development of high-speed railways for other countries. CRH in China has learned from it in terms of expertise and technology. The Tokaido Shinkansen, opened for traffic in 1964, is the world’s first high-speed rail line in commercial operation. It connects Tokyo, Nagoya, and Osaka. Eurostar runs from the island of Great Britain to the European continent across the English Channel, and connects London in England, Paris and Lille in France, and Brussels in Belgium. It takes one from St. Pancras Station in London to Paris in France in 2 h and 16 min, and to Brussels in Belgium in 1 h and 53 min at minimum. It has significantly improved people’s travel efficiency and is the most popular route to link London and Paris. (2)

Road transportation. According to the data released by the National Bureau of Statistics, the total highway mileage in China had reached 4,846,500 km with 142,600 km for expressways as of the end of 2018. There were 232.31 million civilian vehicles in China, with 205.55 million passenger cars and 25.678 million trucks. The passenger volume by highway was 13.67 billion, 76.2% of the total, and the freight volume 39.57 billion tons, 76.8% of the total [18].

The highway network in China is mainly composed of 12 main national highways with “five vertical ones and seven horizontal ones”, transportation hubs and information systems. The “five vertical ones” refer to Tongjiang-Sanya Expressway, BeijingFuzhou Expressway, Beijing-Zhuhai Expressway, Erenhot-Hekou Expressway and Chongqing-Zhanjiang Expressway. The “seven vertical ones” include the Suifenhe-Manzhouli Expressway, Dandong-Lhasa Expressway, Qingdao-Yinchuan Expressway, Lianyungang–Khorgas Expressway, Shanghai-Chengdu Expressway, Shanghai-Ruili Expressway, and Hengyang-Kunming Expressway. (3)

Waterway transportation. China has a vast territory, a long coastline, numerous rivers, lakes, streams and abundant water resources, and thus seen waterway transportation since ancient times.

At present, China has built a port system with a reasonable layout, distinctive levels, complete functions, and complementary advantages. Coastal ports have been built for the five major transportation systems respectively for coal, mine, oil, container, and grain. For inland waterways, the national high-level waterway network with “two horizontal and one vertical lines and two systems” [14]. According to data from

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China Statistical Yearbook 2018 published by the National Bureau of Statistics [19], China had 1913 berths with a capacity of 10,000 tons and above, and 127,000 km of navigable mileage of inland waterways as of the end of 2017. (4)

(5)

(6)

Air transportation. The air transportation industry in China has developed rapidly since the reform and opening up. In the early twenty-first century, China joined the World Trade Organization. The surge in China’s foreign trade volume has further promoted the development momentum. According to the data from the China Statistical Yearbook 2019 released by the National Bureau of Statistics [18], China had 4945 civil aviation routes, including 849 international routes and 4096 domestic routes as of the end of 2018. The passenger traffic volume reached 610 million people, the passenger turnover volume 1071.2 billion people per kilometer. The cargo traffic volume 7.39 million tons, and the cargo turnover volume 26.25 billion tons per kilometer. It had 233 civil airports and 6134 civil aircraft, with 3639 for civil transportation. Based on the experience of the development of the international civil aviation industry, that in China will keep a high growth rate in the future. Pipeline transportation. Pipeline transportation features a large transportation capacity, high continuity, low transportation cost, low energy consumption, high expertise and automation, and ease of management. The delivered products do not require packaging and see a low loss rate, with the equipment and facilities occupying little space. The preparation required is the most convenient among other transportation methods, the construction period short, and the investment low. The disadvantages lie in that few kinds of commodities can be delivered and the transportation speed is low. It can only be applicable to one-way and fixed-point transportation of fluides with a large volume, and thus sees low flexibility. In addition, it is more difficult to maintain than others. Considering its advantages and disadvantages, pipeline transportation is now mainly applicable to oil and gas resources, and sometimes chemical fluids. It is favored and sees rapid development given the many merits. Multimodal transportation. It refers to the transportation process that requires two or more modes of transportation (transportation means) for cooperation. It needs to be pointed out that the two modes of transportation or more should be different, such as sea-land transportation, land-air transportation, and sea-air transportation.

Various modes of transportation have their strengths and weaknesses. For example, waterway transportation features a large capacity and high costs, and yet low speed and weak continuous transportation capacity. Road transportation is flexible and enables door-to-door delivery, yet with a low capacity and severe impact on the environment. The main advantage of railway transportation is that it is highly adaptable to climate, and can realize long-distance and punctual transportation of goods deep inside and across inland. But it requires large investment and a long term of preliminary construction as preparation, and a lot of time for marshalling during the operation. Air transportation is the fastest way of long-distance delivery, yet with a low capactiy, high costs and vulnerability to natural conditions. In summary,

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multimodal transportation can draw on the strengths of different methods for higher efficiency.

3.2.3.3

Network Reliability

Network reliability first appeared in the study of communication networks and then extended to many fields such as computer networks and engineering systems as research goes deeper [20]. The reliability of different networks is define differently: the reliability of the road network, which is the probability that the capacity of the road network with uncertainty can meet a certain traffic demand due to the influence of many external factors [21]; the reliability of bus network, which is the probability that the customers that accept the travel fees can be delivered to the destination on time under the operating conditions of the bus network now, or from another angle the probability that the customers cannot be delivered to the destination on time when the bus system fails [22]. (1)

Network reliability measure. The network reliability measure has three types: the network’s invulnerability, the network’s survivability, and the network’s effectiveness [20].

First, network’s invulnerability measures the difficulty of destroying a network or the anti-attack ability of the network topology in the “worst case”. The invulnerability measure refers to the minimum number of nodes or edges that need to be destroyed to break down the network, generally with cohesion and connectivity [23]. Second, network’s survivability refers to the ability of the network to complete tasks in time when damaged, partly failing or even breaking down [24]. Third, network’s effectiveness refers to the satisfaction of users with the service performance indicators provided by the network when part of it fails, and measures the business performance of the network [25]. (2)

Network reliability calculation. The calculation of network reliability mainly resorts to two methods: analytical method and simulation method.

The basic idea of the analytical method is to transform a complex network into a combination of simple systems, such as a series system and a parallel system. The reliability of these simple systems is again used to calculate that of the original complex system according to the actual logical relationship [26]. He Guoguang and Zhou Liangsheng [27] studied network systems from the perspective of network trees, explored the applicable principles of the minimum path set method and the minimum cut set method, and created a simplified algorithm for network system reliability. Zhang Yan [28] regarded the logistics network as composed of several OD pairs and used the minimum path method processed with the disjoint method to measure the reliability of the logistics network. Zhu Tingting [29] used the minimum path set method to obtain the expression for the calculation of the reliability of the coal logistics network, and proposed a coal logistics network optimization model based on reliability constraints. Cai Jianming [30] used the traditional GO method to

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study the reliability of the time-varying logistics transportation network in the event of an earthquake, and proposed the possibility GO method to predict the reliability of the emergency logistics transportation network in the event of an earthquake. Wen Jing [31] used the time reliability to measure the network service performance when studying about the optimization of the cold chain logistics network for medical drugs, and proposed how to calculate the time reliability in the network. Tang Lei, Xu Bing, Huang Guori, et al. [32] concluded with research that the simulation method is suitable for dealing with complex distribution systems, and the analytical method suitable for distribution systems with a simple structure. The simulation method is a quantitative analysis method that relies on computer technology, places the system under certain environmental conditions, and conducts simulation experiments on the interaction of various elements to obtain the numerical values. According to the type of problem, the simulation method can be divided into random simulation, fuzzy simulation, fuzzy random simulation, etc. [28]. Liang Huishi, Cheng Lin, and Liu Sige [33] used Monte Carlo time series simulation to create an evaluation method for the reliability of distribution networks with microgrids. The main advantages of the analytical method lie in its clear concept and high accuracy of the model. The multiple uncertain factors in the system can hardly be considered simultaneously, and it is not easy to deal with the correlation between variables, however [26]. The simulation method is direct, easy to be mastered and understood, and is suitable for dealing with complex factors, especially the correlation between variables. Therefore, for some complex problems for which the analytical method can hardly be used, the simulation method will be more effective [28].

References 1. Fengyi Y. Urban logistics system layout research. Nanjing: Southeastern University; 2005. 2. Hongmei H. Study on the key influencing factors of urban logistics. Wuhan: Huazhong University of Science and Technology; 2012. 3. Lu Q. Study on space structure characteristics and evolution theory of urban logistics. Beijing: Beijing Jiaotong University; 2012. 4. Jian Z. Analysis on the application situation and countermeasures of logistics technology under the background of frequent express explosion. Logistics Technol. 2013;32(5):96–9. 5. Ti C, Xiaohui L. Quality analysis of logistics industry employees in China. China Bus Mark. 2013;(6):103–107. 6. Tao L, Zeqiang Z, Wenming C. Status and promotion strategy of loading and unloading and handling in logistics activities. Railway Freight Transp. 2004;(06):33–35, 2. 7. Weidong B. Research and analysis of logistics loading and unloading and handling operations. Sci Technol Vis. 2012;26:177–8. 8. State Council of PRC. Medium-and long-term planning for logistics development (2014–2020); 2014. 9. General Office of the State Council of PRC. Special action plan for cost reduction and efficiency increase in the logistics industry (2016–2018); 2016. 10. General Office of the State Council of PRC. Opinions on promoting the collaborative development of e-commerce and express logistics; 2018.

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11. Jie Y, Ying T. Study on the influence of cross-transport flexible on logistics reliability. New Technol New Process. 2007;8:9–11. 12. Qingyi W. The change of logistics demand has a great impact on enterprises, and logistics enterprises adjust their business structure to deal with. Truck Logistics; 2009(2):26–27. 13. Hongtao Y. Effect of e-commerce on consumer demand and enterprise marketing strategy. China Sci Technol Inform. 2005;6:59. 14. Jingmin Z. Logistics operations management. Beijing: China Fortune Publishing House; 2015. 15. Chao M, Junli G, Xiaodong Z, Xiangguo L. History and status of Amtrak development. Railway Transport Econ. 2011;33(09):58–61. 16. Xiaoli Y. Current status and revelation of Amtrak development. China Transp Rev. 2010;02:67– 71. 17. http://www.nra.gov.cn/xwzx/zlzx/jytddt/201309/t20130917_2574.shtml 18. National Bureau of Statistics. China statistical yearbook 2019. Beijing: China Statistics Press; 2019. 19. National Bureau of Statistics. China statistical yearbook 2018. Beijing: China Statistics Press; 2018. 20. Deliang C. Key issues and application research of the reliability of the logistics network. Changsha: Central South University; 2010. 21. Lichun C, Lixin M. Research and development of road network reliability model. J Central Sounth Highway Eng. 2005;04:119–23. 22. Yuming H. Urban bus network reliability study. Beijing: Beijing Jiaotong University; 2007. 23. Jun W, Yuejin T. Study on anti-destructive measures of complex networks. J Syst Eng. 2005;(02);128–131. 24. Fan Y, Jianchun J, Songqiao C. Overview of network viability studies. Appl Res Comput. 2001;06:12–4. 25. Xiaoli H, Hong L, Xiongda L. Performance parameters and network effectiveness analysis model in broadband IP network QoS. J Chongqing Univ Posts Telecomm (Natural Science Edition). 2005;(03):344–346. 26. Haixu L. Urban traffic network reliability study. Chengdu: Southwestern Jiaotong University; 2004. 27. Guoguang H, Liangsheng Z. Reliability simplification algorithm of network system based on reliability unit. J Mach Des. 2008;(07):62–65. 28. Yan Z. Fresh products logistics network based on reliability. Chengdu: Southwestern Jiaotong University; 2009. 29. Tingting Z. Study on optimization of coal logistics network based on reliability constraint. Beijing: Beijing Jiaotong University; 2013. 30. Jianming C. Study on emergency logistics degeneration and reliability of earthquake disaster. Changsha: Central South University; 2012. 31. Jing W. Optimization of pharmaceutical cold chain logistics network based on reliability. Wuhan: Wuhan University of Technology; 2016. 32. Lei T, Bing X, Guori H et al. Reliability assessment of power distribution system. Proc CSUEPSA. 2016;28(01):32–38. 33. Huishi L, Lin C, Sige L. Reliability assessment of micro networks based on Monte Carlo simulation. Power Syst Technol. 2011;35(10):76–81.

Chapter 4

Factors Influencing the Reliability of Urban Logistics System

Modern logistics industry has become a “driving force” behind economic development and an important indicator of a city’s economic strength [1]. At present, China is undergoing an unprecedented process of urbanization. According to statistics, there are more than 600 cities above the county level in China to constitute a huge urban network. A city is the center of business flow, material flow, information flow, capital flow and talent flow, and influences and promotes the development of rural areas. For industrial city, commercial city and tourist city, to build a comprehensive logistics system with point-line-surface integration is a must, so as to ensure the orderly and efficient flow of business, materials, information, capital, and talents [2]. However, the optimization of urban logistics system is a complex system closely related multiple sectors and all aspects of the city. Before proceeding to the research on reliability optimization for the urban logistics system, it is necessary first to clarify the influencing factors of the urban logistics system and how they work on the reliability of urban logistics and are associated with each other, only based on which the optimization of the urban logistics system makes sense.

4.1 The Relationship Between Urban Logistics and Urban Development The economic development that brings together the factors of production is ultimately to spread commodities, with a developed circulation industry as the guarantee to realize the value and use value of commodities and ensure the normal operation of urban economy. Urban development and urban logistics influence each other. Urban logistics is an important pillar to ensure the basic operation of a city. Trade, industry, and people’s livelihood in the city cannot be separated from urban logistics. Urban logistics is one of the main components of urban economy. It has an important role in stimulating the overall economic operation of the city and ensuring the smooth © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. Zhang, Reliability Optimization of Urban Logistics Systems, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-19-0630-5_4

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operation of urban economy. According to statistics, for every 2% of increase in the logistics industry in developed countries, the GDP of urban areas will rise by 1% [3]. The construction of an efficient logistics system in the city can optimize the allocation of resources, ensure the continuity of production, the accuracy and reliability of delivery, and enable enterprises to pool forces to improve the quality of products and services; and reduce the total cost of society, optimize the allocation of resources, and improve the city’s economic competitiveness. Logistics promotes the development of many industries in the city, contains huge development potential, and represents a new growth point for urban economy. (1)

(2)

(3)

A sound urban logistics system can effectively promote the development of urban economy. First, urban logistics plays an important role in optimizing the industrial structure of cities. The development of urban logistics promotes the development tertiary industry and related industries, and optimizes the structure of primary, secondary and tertiary industries. A development model based on the tertiary industry should be formed to enhance the competitiveness of cities. Second, the development of urban logistics can enhance the communication between the city and surrounding areas, and help deliver goods from surrounding areas to the city to meet the needs of the city and from the city to the surrounding area to achieve economic and social complementarity, thereby greatly enhancing the city’s influencing scope and capacity, realizing the common development of the city and surrounding areas, and laying the foundation for the sustainable and sound development of urban economy. Third, the development of urban logistics can enhance the comprehensive functions of the city and increase its agglomeration power. By solving the problems encountered during the development of logistics, the city will continuously improve its planning and layout, improve the efficiency of logistics, and see its comprehensive functions continuously improved [4]. Modern logistics is a new growth point for urban economy. The development of the logistics industry can drive forward that of relevant sectors and industries, and provide support for industries such as commerce and industry. The effective use of logistics, a new growth point in cities, helps motivate local economic development. For example, Beijing, Shanghai and other cities have adopted measures to promote the development of logistics and increase investment in logistics infrastructure to ensure the sound and rapid development of the logistics industry. The logistics industry in Shanghai is making a huge contribution to the economy at an annual growth rate of 21.3%, and plays a key role in stimulating the local economy [5]. The modern logistics industry is conducive to promoting the optimization of the regional industrial structure. The rationalization of the industrial structure is measured with the development level of the tertiary industry. The logistics industry is a comprehensive one covering transportation, warehousing, processing, and information industries, and an important part of the national economy. The logistics industry itself belongs to the tertiary industry. The development of urban logistics brings together business flow, capital flow, and

4.1 The Relationship Between Urban Logistics and Urban Development

(4)

(5)

(6)

81

information flow, and needs the support from transportation equipment manufacturing, financial, high-tech and other related industries. It helps promote the development of the tertiary industry, increase the proportion of the tertiary industry in the three major kinds, optimize the structure of the primary, secondary, and tertiary industries, and further rationalize the industrial structure [6]. Modern logistics industry is conducive to enhancing the core competitiveness of cities. The logistics industry has a formula for calculating the core competitiveness of a city: core competitiveness of a city = productivity × circulation capacity [7]. Inefficient circulation makes it fail to meet the requirements for local production capacity, and also causes poor access between the city and the outside world. On the contrary, efficient circulation can make up for the lack of local productivity through internal and external exchanges and complementary advantages, and hence enhance the influence and competitiveness of the city. In March 2010, Yining City broke the “bottleneck” of urban transportation by building a three-dimensional transportation network and adopting the “agricultural products-supermarket partnering” measures, so as to shape a logistics service system compatible with the production factor market. Yining City has accelerated the development of modern logistics, and effectively enhanced its core competitiveness. The modern logistics industry promotes the construction of urban transportation infrastructure and the formation of city-centric regional economy, and helps attract foreign investment and enhance the overall competitiveness of the city [8]. The development of cities promotes the construction of urban logistics system. With the continuous development of the city, it has seen more potential for logistics demand. The materials needed for production and living are inseparable from the logistics system in the densely populated city. With the improvement of the living standards, urban residents have put forward higher requirements for the timeliness, accuracy and efficiency of logistics. The development of ecommerce has also promoted the further growth of urban logistics. The diversification of products in quantity, type, delivery location, and delivery time makes it necessary to establish a reliable and flexible system for urban logistics to meet the needs of customers in their daily life and to ensure the smooth operation of the system in the event of emergencies. The city is a platform for the development of modern logistics. The city has a large number of universities, scientific research institutions, abundant resources, and advanced technical support, which provide talents and technology for the development of logistics. The well-equipped infrastructure and convenient transportation in the city provide effective support for the development of the logistics industry. Cities are areas where industry and commerce are concentrated, and thus witness a huge demand for logistics. Logistics needs to rely on cities to open up markets [9].

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4.1.1 Urban Logistics and Commerce Logistics provides services for commodity production and commodity exchange in the commerce industry and solves the problem regarding the transfer of use value. With the improvement of peopled commodity exchange and changes in consumption patterns and retail formats, logistics has become increasingly important in the retail industry, and one of the three pillars as procurement and sales of enhancing the competitiveness of retail enterprises. It has also played an important role in coordination, overall optimization and forecasting [4, 10]. First, a flexible and reliable urban logistics system can reduce the costs and circulation expenses for commercial enterprises. After commodities are produced, their use value must be realized through logistics. Business flow completes the transfer of ownership of goods, and logistics material exchange. Therefore, the efficiency of logistics transportation, packaging, distribution and other functions directly affects the circulation of commodities and the development of the commerce industry, which indicates the important position of logistics in the commerce industry. The effective support of the urban logistics system can help improve the supply accuracy and distribution efficiency for commercial enterprises, and hence reduce logistics costs. Second, the urban logistics system promotes the diversified development of types of operation in the city. (1) It has promoted the upgrading of retail business in operation and chain operation effectively, and guided and formed new consumption concepts. With economic development, the traditional retail model has been unable to meet the increasing demand. The development of commercial logistics and distribution has prompted the emergence of chain operation models such as supermarkets and convenience stores, promoted the upgrading of business methods, and guided and formed new consumption concepts. (2) The urban logistics and distribution have brought forward new marketing methods such as general distribution and general agency for commercial wholesale enterprises, for which the commercial circulation is no longer limited to the traditional mode. The previously traditional purchase-sales model has become more intelligent, information-powered and efficient. Third, logistics provides great support for the wholesale system. Wholesalers serve as the bridge between production companies and retailers. They do not directly serve consumers, but are located in the middle of commodity circulation. The logistics industry has provided necessary material and technical support in establishing a modern wholesale system for production and retail services. Wholesale enterprises have changed their sole function of wholesaling and realized low-cost and highefficiency circulation of commodities. Fourth, logistics provides a basis for forecasting retail inventory. With the economic development and the improvement of people’s living standards, the needs of consumers have become more diversified and personalized. Also given the impact of seasons and emergencies, these factors pose greater challenges to inventory management. Due to too low an inventory, retail companies will lose market competitiveness and flexibility; too high an inventory will cause overstocking, increase in inventory costs, and harm to inventory control. Therefore, to predict consumption

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demand is critical and will directly affect the company’s inventory strategy and business development [4].

4.1.2 Urban Logistics and Industry The development of foreign manufacturing and logistics shows that manufacturing is the cornerstone of the development of logistics, and the development of logistics has promoted the development of manufacturing. That is, the two promote each other. The development of urban manufacturing inevitably requires the level of logistics to be compatible as the basis [11]. According to data released by the National Development and Reform Commission [12], the total social logistics volume across China was 177.3 trillion yuan in 2012, a year-on-year increase of 9.8% at comparable prices. In terms of composition, the total volume of logistics for industrial products achieved 162 trillion yuan, a year-on-year increase of 10% at comparable prices. The data shows that industry is the main driving force for the growth of total social logistics. The Development Plan for the Logistics Industry in Beijing during the Twelfth Five-Year Plan Period clearly points out that the city will “vigorously develop highend modern manufacturing, cultivate and expand modern industrial clusters, and focus on emerging industries such as the new-generation information technology, biomedicine, new energy, energy conservation and environmental protection, new energy vehicles, new materials, high-end equipment manufacturing, and aerospace.” These high-end modern manufacturing products with high integration and high added value need the effective support of the logistics industry and refinement and timeliness of response of the logistics system. The globalization and large-scale development today have promoted the production and sales of commodities, and industrial development changed from a traditional model to a new and integrated model. Industrial development has shifted its orientation from cheap labor to technology, and its internal production division more refined and specialized. Urban logistics has built a circulation platform for industrial products, and functions for coordination, integrated warehousing, distribution, and information processing. The circulation of industrial raw materials, semi-finished products and finished products is organized, and costs of transactions between enterprises reduced. The industrial logistics is highly specialized, and differs among sectors. Heavy industry and light industry have different requirements and forms of organization for logistics. Therefore, the traditional and extensive logistics model cannot keep up with the complexity of industrial logistics. An efficient logistics system can provide driving force and support for the further development of industrial enterprises [13]. For example, the logistics company J is a third-party logistics company that provides supporting products and services for industrial manufacturers. With the support of information technology, J has created greater value for customers through service innovation. By providing centralized procurement services for the company B, J has cut down the cost of general tools by 6% and that of others by about 1%,

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reduced 50 million yuan of capital occupied, and saved 1 million yuan of staffing costs, which means a reduced total cost of over 6 million yuan for B. The integrated service by J has enabled relevant suppliers to expand their market share and concentrate on their main business and increase in product varieties, quality and other aspects of core competitiveness, and to reduce their operational risk. It can be seen that logistics is an indispensable step in industrial production, an important part of industrial production, and an important approach to promoting the transformation of industrial development mode and the upgrading of industrial structure. Of the total volume of social logistics, industrial products accounted for about 90%. Logistics links such as warehousing and transportation consumed more than 90% of the time during the manufacturing and circulation of industrial products. Excessively high industrial logistics costs and low logistics efficiency have become bottlenecks restricting the sound operation of the industrial system [14]. Therefore, it is imperative to develop logistics, transform the mode of industrial development, and improve the core competitiveness of enterprises. The priority should be given to promoting the joint development of manufacturing and logistics, encouraging manufacturing companies to integrate and optimize business processes, divesting logistics businesses, and innovating logistics management models. It helps companies tap the “third source of profit”, reduce the total cost of logistics, provide additional value-added services, and increase the industrial value-added rate. It helps the industry to accelerate the transformation towards service-oriented manufacturing and promote the transformation of development mode. It helps to realize the supply chain integration and promote the development on the new industrialization path [15]. To vigorously develop logistics will also help to remove the long-existing traditional concepts about industrial enterprises in China being “einghina l enterprisesitional c “einghina l enterprisesition, and transform the traditional production organization and management mode with a “self-contained system” and “self-service”. A modern logistics service system compatible with the modern industrial system should be established, and the development of logistics become an important breakthrough for the transformation and upgrading of industrial enterprises. Modern logistics integrates logistics activities such as transportation, storage, loading and unloading, packaging, and information processing, and optimizes and integrates the inventory, transportation, and materials of industrial enterprises. Modern logistics helps adapt the entire supply chain to logistics services with information-based and professional logistics services. With modern methods, networked organization, and intelligent management, it will help shorten the processing of orders, ensure the smooth progress of production, and reduce the total cost for the enterprise [13].

4.1.3 Urban Logistics and Urban Transportation An important process of logistics is to deliver goods to different places and is inseparable from transportation. Transportation is an important function to directly affect

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how logistics can meet customer needs. Timeliness is a distinctive feature of logistics. Urban logistics has higher requirements for timeliness, such as the timely supply of daily necessities for urban residents and that of raw materials for production for enterprises. Fast and reliable urban logistics are affected by urban infrastructure. The construction of urban expressways and inter-city expressways has played a huge role in promoting the development of modern logistics industry. The expressway has increased the speed of transportation and improved the level of roads in the city. For example, after the completion of the Meishin Expressway in Japan, the travel time has been reduced by about 75%. The expressway connects important nodes in the city and those between the city and surrounding areas, enhances the influence and traffic capacity of the integrated transportation network, and makes the communication between the city and surrounding areas smoother and faster. After Shenyang-Dalian Expressway was put into operation, the transportation cost dropped by 15.4% [16], and the reliability of distribution and the intact rate of goods enhanced. In Beijing for example, the total highway mileage reached 22,226 km in 2017, of which the expressways totaled 1013 km and accounted for 4.6% of the total. The continuous improvement of the urban road transportation network has helped shorten the distance from the city center to the suburbs, enhance its influencing capacity, and promoted the development of urban logistics [17]. A large part of urban logistics serves the end consumers, and the logistics demand is shown in small batches and with multiple varieties and high frequency. With the improvement of peoples serves standards, consumers have higher requirements for the timeliness of transportation and the safety of goods. Urban logistics needs to be operated rationally, service levels improved, and customer requirements met. Urban logistics has higher requirements for transportation, and the construction of expressways has played an important role in increasing the speed of transportation. Expressways play an important role in connecting important logistics nodes in the urban and suburban areas. The belt expressways and urban arterial roads help reduce the time on delivery and shorten the distance between nodes. Consumers can buy fresh vegetable and fruits just collected from the suburbs. With the construction of expressway-based logistics network, regional logistics and distribution centered on the city have developed faster, exerted a positive impact on improving the efficiency of regional logistics, and promoted communication between the city and surrounding areas [16]. Urban logistics not only needs to meet the needs for production and living within the city, but also plays an important role in linking with surrounding areas. Products from the city need to be sent out, and those from outside into the city. With the rapid development of various cities, they have more frequent communication. The further regional integration has expanded the demand for logistics, created great demand for circulation within and between cities, and provided opportunities for the development of urban logistics. With the progress of urbanization in recent years, cities have continued to expand, and the urban population increased sharply. Urban traffic problems have stood out in restricting urban development. The burden is increasing on urban traffic, traffic

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congestion more severe, and logistics vehicles occupying main roads …. In economically developed cities, vehicles move almost at the same speed as walking. If urban traffic problems cannot be solved, the development of the city will be hindered. In order to improve the efficiency of urban transportation, the government has adopted a series of measures. For example, the number restriction policy, license plate lottery policy, and time limits for the passage of freight vehicles in urban areas are specified in Beijing. However, these countermeasures are more focused on the evacuation of traffic flow, and the problems caused by urban freight transportation and distribution not solved. For example, freight vehicles frequently enter urban areas, and those waiting for loading or unloading in some trading markets or around distribution centers occupy the main traffic roads, which not only causes traffic congestion, but also affects the efficiency of commodity circulation. Near the refrigeration plant in the southwestern suburbs of Beijing and near Xinfadi, too many distribution and wholesale vehicles, coupled with unreasonable road planning, often cause traffic congestion and have certain impact on surrounding roads.

4.1.4 Urban Logistics and People’s Livelihood The construction of urban logistics system helps to ensure and improve people infadi, too man. It is clearly stated in the report of the 18th National Congress of the Communist Party of China that n In strengthening social development, we must give high priority to ensuring and improving the people’s wellbeing. We should bring as much benefit as possible to the people, resolve as many difficulties as possible for them, and solve the most pressing and real problems of the greatest concern to them.” The distribution in urban logistics is a livelihood project related to social stability and daily life of residents. For example, the supply and distribution of living necessities in “rice bags”, “vegetable baskets”, and “agricultural products-supermarket partnering” measures, and the urban logistics system and supply of urban emergency materials in e-commerce all require a reliable and effective urban logistics system as the guarantee. If the supply cannot be efficiently guaranteed in a short period of time, the lives of people will be affected, and a huge impact brought on social security and stability, and losses on a wider scale caused. With the improvement of Internet technology and the change of residents’ consumption concepts, the e-commerce model featuring shopping, transaction, and payment through the Internet has developed rapidly. With low cost and high efficiency, e-commerce has become more popular among ordinary consumers [18]. Online shopping has become part of our lives. Daily necessities such as food, clothing, electrical appliances, and books can be purchased online. Except for a small number of products that provide services, most transactions rely on logistics. Therefore, the development of e-commerce has directly promoted the rapid development of the express delivery industry. Table 4.1 shows the business volume and business income of express delivery service companies above designated size.

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Table 4.1 Business volume and business income of express delivery service companies above designated size City

Cumulative express Year-on-year business volume growth (%) (10,000 pieces)

Accumulated income from delivery (10,000 yuan)

Year-on-year growth (%)

Beijing

141,447.3

27.4

1,816,522.5

23.1

Shanghai

170,778.0

33.0

4,552,476.2

26.0

Tianjin

25,624.4

106.6

435,370.4

73.7

Chongqing

20,525.4

47.8

286,533.2

42.5

Source [19] http://www.spb.gov.cn/xw/dtxx_15079/201601/t20160114_710673.html

According to data released by the State Post Bureau, express service companies nationwide delivered 20.67 billion pieces in 2015, a year-on-year increase of 48.0%, and earned a total income of 276.96 billion yuan, a year-on-year increase of 35.4%. Data show that the income from the express delivery business accounted for 68.6% of the total in the postal industry in 2015. In the first half of 2015, a total income of 119.57 billion yuan was earned from express delivery business, a year-on-year increase of 33.2%, and 8.46 billion pieces delivered, a year-on-year increase of 43.3%. But the fast-growing and prosperous express delivery industry also sees many problems. For example, with the promotion on “no motion le, perityytry also 0.3%. But the fast-growing at a show companies above packages purchased online increased significantly. Although the e-commerce and express delivery industry made sufficient preparation, situations such as “overload” and loads such as were still inevitable. The goods bought on an e-commerce platform might be lost or damaged during the delivery. The demand from e-commerce cannot be met by the processing capacity of the express industry. The surge in volume of transactions leads to decrease in the logistics speed. It affects not only the development of the enterprise, but also peoples the ent of the enterprise, but aruction of an urban logistics system for e-commerce brooks no delay. A perfect urban logistics system is reliable in emergencies, and can adapt to the development of e-commerce in speed, increase the logistics speed, and reduce the error rate [20]. The construction of the cold chain logistics system is also related to people’s livelihood. To ensure that fresh food is safely delivered from the source of production to the hands of consumers is an important part of improving the circulation system and strengthening the security system. Cold chain logistics can extend the sales radius and preservation period of commodities. For example, vegetables are greatly subject to the regional and seasonal influence. Low output will lead to insufficient market supply, and high output oversupply in the market. In case of oversupply, vegetables will be piled up. With no proper preservation measures, a large amount of them may be thrown away, which seriously affects the income for farmers, causes huge economic losses, and largely reduces production efficiency. The cold chain logistics can keep vegetables fresh by dint of refrigeration, create a suitable environment, maintain the freshness of vegetables, extend the duration of their supply, and enable their sales

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across seasons and regions, so as to ensure food safety, avoid waste, increase income for farmers, and improve the production efficiency [21]. The perishability of food requires various links in the cold chain to be more organized and coordinated. Given that the current urban cold chain logistics system in China is not sound, the whole process from production, processing, warehousing, transportation and sales, and to consumption should be done at a specified low temperature to ensure the freshness of the products which tend to decay. Therefore, the establishment of an urban logistics system can well reduce the decay rate of commodities in circulation, provide a freshness keeping environment, and increase the time of response.

4.1.5 Urban Logistics and Urban Environment The environment of the city directly affects its development. The deterioration of the urban environment will affect not only the sound development of urban economy, but also the lives of residents. The core of logistics lies in transportation, which provides safe and fast services, but also introduce an adverse impact on the urban environment. With rationalized and standardized logistics activities, the optimal allocation of resources can be realized, the sound development of logistics ensured, the goal of protecting urban environmental protection achieved, and an environment-friendly logistics form created, so as to improve the living environment for urban residents and give better play to the functions of the city [22]. The city is relatively closed space, and the system sees a limited self-circulation capacity to purify the environment. With the expansion of cities, “the prourban maladies” have become more prominent and further restricted the development of urban economy and core competitiveness [23]. According to welfare economics, any economic activity may have external diseconomy. Specifically, the external diseconomy that can be observed in the urban logistics and distribution industry is mainly manifested in four aspects: the occupation of scarce resources (land, roads, energy, etc.) in the city, traffic congestion in the city, no load on road vehicles, and adverse effects of pollutant emissions on the living quality of residents [24]. According to research by Wang Guowen and others, pollutant emissions from the urban logistics industry are an important source of pollution in cities. Trucks emit about one-fifth of the main pollutants, and have seriously affected the implementation of urban sustainable development strategy [25, 26]. With the development of cities, demand of the city continues to grow with the volume of logistics transportation. The increase in logistics has brought a greater flow of urban traffic, and the exhaust emissions from vehicles have caused serious pollution to air in the city. In addition, when some roads have an insufficient traffic capacity, traffic jams and other phenomena will be caused. Urban traffic problems have reduced the efficiency of logistics. With the gradual increase in the proportion of the tertiary industry in the urban industrial structure, consumer demand is becoming more diversified and individualized, and logistics objects lighter, thinner

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89

and smaller. Changes in the circulation structure may cause more frequent transportation by enterprises to meet the needs of consumers, and more serious damage to the urban environment. Therefore, how to resolve the contradiction between the development of urban logistics and the urban environment is essential. In order to meet the needs for production and living in the city, logistics nodes are distributed in the urban area to meet the needs for goods transfer and distribution, and for commercial, production and household consumption. The high frequency of logistics vehicles entering and leaving the main urban area results in air pollution, deterioration of the ecological environment and other issues. Urban logistics should meet customer needs at a low cost and high efficiency, and be based on lowering noise and pollution. The development of foreign urban logistics shows that factors such as the protection of the natural environment and the human environment should be considered in the planning for logistics node facilities, and the interference with urban life minimized. Furthermore, too scattered and single logistics nodes lead to problems such as cross transportation and repeated transportation, so as to render the transportation inefficient and bring greater pressure to urban transportation. Cities are areas where residents produce and live, and the quality of their environment directly affects that of life of residents and the performance of urban functions. The dense population and high concentration of industries have led to the deterioration of the urban environment, and the increase in traffic and transportation contributes to urban environmental pollution as a key factor. The increase in urban traffic has directly led to a surge in the number of vehicles in the city, exhaust emissions, and energy consumption, so as to complicate the environmental problems in the city [27]. During the storage of goods, physical and chemical properties may be changed and harmful substances produced due to improper handling. For example, radioactive and flammable and explosive materials may cause pollution and damage to the surrounding environment due to improper storage. In order to prevent the decay of goods during the storage, companies will perform treat the warehouse with the disinfection lamp or spray pesticides and other chemicals, and thus pollute the surrounding ecological environment. Excessive packaging and disposable packaging of commodities will cause serious pollution to the urban environment. Some packaging materials during logistics circulation are non-degradable, and will cause environmental pollution; excessive packaging of commodities will cause a waste of resources and increase in urban waste, which is not conducive to sustainable development. Unreasonable circulation and processing methods will also have a negative impact on the urban environment. The offcuts from scattered processing in the circulation and processing can hardly be put into reuse. Exhaust gas, waste water and waste generated during processing will pose a threat to the urban environment. The damage to goods during loading, unloading and handling due to improper operation will have an impact on the urban environment. For example, the leakage of chemical liquids can cause soil and water pollution. The impact of transportation on the urban environment is mainly reflected in waste, noise and gas pollution. Vehicle exhaust is an important source of pollution to the urban environment. The International Union of Air Pollution Prevention and

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Environmental Protection Associations based in the UK published a research report which states that the number of people who die from air pollution in the UK each year is ten times that in traffic accidents. Vehicle exhaust is directly harmful to human health, and the sulfur dioxide produced will cause acidification of water and soil and affect the growth of crops [28]. To carry out reasonable planning and design for urban logistics nodes is an effective way to alleviate urban congestion and improve the urban environment. With systematic and strategic adjustment and coordination of the traditional functions of distribution centers, warehouses, enterprises and logistics, the efficiency of urban logistics will be improved. The transportation function of the logistics route and the transportation, warehousing, packaging and other functions of the nodes should be strengthened. Logistics involves production, circulation, and consumption, and is closely related to many industries. In the specific planning process, the interests of each part should be considered, and scientific and reasonable design carried out on the basis of urban planning, so as to increase the sales-output ratio of urban logistics and ensure the rationalization of city logistics. An efficient and reliable urban logistics system plays an important role in improving the urban environment. The construction of urban logistics system can help reduce the area of land occupied for routes, nodes and related infrastructure, and optimize the configuration of vehicles for less pollution of noise and exhaust gas to the city For example, to solve problems such as urban traffic pollution, the Japanese government has built a market for circulation and assigned certain surrounding areas for the construction of urban infrastructure based on urban planning, so as to alleviate traffic congestion and improve the efficiency of logistics [27]. Joint delivery is another way to improve the urban environment. Manufacturers, wholesalers, retailers and other logistics service demand sides can collaborate with each other to provide services through third-party logistics companies. To share logistics services helps not only reduce the cost of logistics for enterprises, but also save energy, ease jams, and improve the urban environment. Urban logistics is closely related to the development of the city. The reliability of the urban logistics system is related not only to the efficiency of urban logistics operation, but also to the competitiveness of the entire city and its economic development. Therefore, it is very important to study the reliability of the urban logistics system. The urban logistics system as a whole will be affected by internal and external environmental factors, internal operation, and others. First of all, it is necessary to identify the factors affecting the reliability of the urban logistics system. The book starts with each link of logistics logically, and uses matter-element analysis to withdraw influencing factors from supply, stocking, distribution, storage, processing, and transportation.

4.2 Analysis of Influencing Factors

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4.2 Analysis of Influencing Factors Foreign scholars mainly focus on how to deal with the negative effects of urban logistics development, and thus study about the influencing factors of urban logistics with regard to the improvement of urban logistics measures. Munuzuri considered the relationship between urban residents, urban logistics and urban transportation, and proposed an urban freight solution covering public infrastructure, land use management, access, traffic management, law enforcement, and publicity, so as to provide support for the government in decision-making [29]. Shao and others established a systematic and comprehensive evaluation indicator system in accordance with the characteristics and meaning of the regional logistics system in China with the globalization of supply chain. They evaluated the development trend of China’s regional logistics industry based on the analytic hierarchy process and fuzzy decision-making principles, and discussed the impact of key factors on the competitiveness of regional logistics industry [30]. Kuse, Endo and Iwao analyzed the relationship between logistics and city formation, illustrated the importance of logistics planning, and proposed a framework for urban logistics planning and the steps of logistics planning from the three aspects of logistics infrastructure, logistics network and regional planning [31]. Russo and Comi analyzed existing results of research on urban logistics policies and proposed logistics measures suitable for urban areas, including physical infrastructure measures, intelligent transportation system measures, logistics equipment measures and control measures [32]. The uncertainty of demand has imposed challenges to the reliability of the urban logistics system. The urban logistics system is affected by many factors from emergencies to random fluctuations in customer demand. The large urban population, complex and changeable traffic conditions, and the frequent occurrence of various natural disasters, public health incidents and social security incidents in recent years have caused increasingly serious challenges to urban logistics. If the supply cannot be ensured quickly in case of emergencies, panic in the society and greater losses will be caused. With the economic development, people economy, the to be ensured quad, and the market demand developed towards diversification and customization. Also with the development of information technology and the improvement of production technology, products manufactured by enterprises are becoming increasingly diversified. The demands for urban logistics vary in time, and sometimes to meet the needs of customers as soon as possible is not necessarily good. Customers sometimes want the delivery on working days or according to the needs of production. These factors all pose greater challenges to the urban logistics system. In summary, many factors have affected the reliability of the distribution network, including monitoring capabilities, distribution center location, budgeting, technical equipment, demand changes, weather conditions and relevant governmental policies and regulations. As a whole, the urban distribution system will be affected by external environmental factors, such as weather conditions, traffic conditions, and emergencies in the city. They are out of control for distribution companies and can be

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Fig. 4.1 Factors affecting the reliability of urban logistics system

regarded as force majeure to the urban logistics system. The operation runs through every node and link of the distribution network across the city, and is also the most closely related to the reliability of the distribution network. Based on the above analysis, the factors affecting the reliability of the urban logistics system can be investigated from the following five aspects as shown in Fig. 4.1, including information, operational capabilities, reliability of technical equipment, policies and regulations, and force majeure. In-depth analysis of these five aspects will be carried out, specific indicators obtained, and their qualitative and quantitative analysis done, so as to lay a foundation for finding out the key influencing factors. Of course, these five influencing factors are not applicable to all systems. The influencing factors of different systems vary according to different actual situations. Specific analysis should be done for specific issues, so as to acquire more scientific and reasonable results.

4.2.1 Information 4.2.1.1

Mechanism of Action

Goods move around with the flow of information. With the rapid development of logistics industry today, information technology has become the key to the development of modern logistics, and played an essential role in the urban logistics system. Logistics information is a general term for knowledge, materials, images, data, and documents that reflect the details of various logistics activities. The logistics information system sets the flow of information throughout the distribution system generally with functions such as collection, transmission, storage, processing, and output (see Fig. 4.2). It mainly functions to integrate and coordinate the information between

4.2 Analysis of Influencing Factors

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Fig. 4.2 The role of information in the logistics system

various elements, and direct the selection of distribution routes [33]. There are many ways to classify logistics information. This book mainly analyzes the impact of information on the reliability of urban logistics system on a functional basis. Logistics information is crucial to the entire logistics system. For example, according to the needs of customers, retailers provide ordering information for upstream wholesalers, and wholesalers confirm inventory information and send it to the third-party logistics companies. If there is a shortage of goods for the wholesaler, it will provide the out-of-stock information for the manufacturer for immediate replenishment. As shown in Fig. 4.3, manufacturers must obtain the market demand information in a timely manner in order to control the production process. They must ensure timely supply and no backlog. Suppliers and retailers need to ensure sufficient inventory according to the downstream requirements. Consumer needs are diverse and random, so information must be transmitted quickly and accurately to meet them. Logistics companies organize transportation according to orders placed by other companies to ensure the smooth operation of the supply chain. In the whole process, it can be seen that the logistics information from producers through wholesalers to retailers has played a role in supporting logistics activities and ensuring the effective operation of the entire supply chain. It is conceivable that if the information

Fig. 4.3 The importance of logistics information

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4 Factors Influencing the Reliability of Urban Logistics System

Fig. 4.4 Flow chart of transportation

is delayed in a certain link, the entire logistics system will collapse, retailers and wholesalers will be out of stock, and consumer needs not be met, the efficiency of the logistics system reduced, and the operating costs for enterprises increased. Logistics information can be divided into transportation information, warehousing information, loading and unloading information, packaging information, and processing information according to its functions and fields. (1)

Transportation information, including that about product quality, safety and location during transportation. For cold chain logistics, it also includes the temperature and humidity control during transportation, as shown in Fig. 4.4.

The delay in the management of the information about goods in transit will cause that in the transportation and damage to goods. Only by improving the ability to track and control the information about goods in transit can the logistics information of the entire system be integrated and shared, customer satisfaction improved, and market competitiveness enhanced. The normal operation of an enterprise needs to rely on urban logistics. Only when goods are delivered to customers in a timely and reliable manner can the enterprise’s business be realized. For example, transactions on a certain e-commerce platform are only valid when goods are delivered to consumers at a high speed and low efficiency through logistics. Convince stores, for another example, need replenishment every day, and only timely logistics can ensure their continuous operation. It can easily be seen that the reliability of logistics is a prerequisite for the sustainable development of an enterprise. If goods are decreased in quantity or damaged in quality during the transportation, sales for the enterprise will be reduced. Therefore, how to organize and efficiently track transportation, and ensure the quality and safety of goods in transit is crucial [34]. Products should be tracked in transit. Through GPS, camera, voice call and other technological means, the information about vehicle positioning is fed back to the control center without delay, and the information such as vehicle latitude, longitude,

4.2 Analysis of Influencing Factors

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speed and flow direction directly reflected in real time, so that the control center can comprehensively consider the warehouse location, capacity, and customer needs, generate the optimal route, and transmit it to the driver through the GPRS system to ensure the timely and safe arrival of the goods. In the event of a traffic accident, the driver can transmit the information back to the company’s monitoring center, and the company take emergency measures based on the vehicle location to satisfy the customer needs without delay. The information about the vehicle in transit is disclosed for customer to acquire in real time and see his or her demand for valueadded services met. In case of low efficiency of information reading during transportation, customer needs cannot be met quickly and accurately. If a company only withdraws cargo information when it leaves the warehouse and is delivered, but ignores the management of the information during the transportation, the loss of the goods will be caused, and the quality of service affected. It is very important to carry out effective monitoring, emergency rescue, and transparent management of vehicles with high mobility and in large quantities, and to provide customers with value-added services for inquiries. The complete real-time monitoring of the logistics process should be ensured, the dynamic information about goods or carriers read correctly and quickly and used for effective monitoring and dispatch, and punctual arrival guaranteed, which play a critical role in improving customer satisfaction, increasing logistics efficiency, and reducing logistics costs. (2)

Warehousing information. Warehousing information mainly includes warehouse-in information, ex-warehouse information and inventory information, as shown in Fig. 4.5.

➀

Warehouse-in information: Before sent into the warehouse, the products should be classified, with their attributes identified, corresponding location specified, and all the information fed back to the computer system. The warehouse-in form should then be made, and trucks for the goods specified according to the form. After receiving the form, the warehouse keeper should be clear about the information on it, and place all the goods that need to be put into the warehouse on the designated truck for warehousing. When all the codes of the goods that need to be put into the warehouse are placed on the shelves, the information should be fed back to the computer system for real-time recording. During the

Fig. 4.5 Flow chart of warehousing

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4 Factors Influencing the Reliability of Urban Logistics System

warehousing, the information is consulted for confirming the storage location upon receiving and warehousing, inspecting the quality and quantity of the goods, and updating the inventory after the confirmation of the warehousing. If the goods are not inspected during the receiving, the quantity, quality, shape and other information of the goods cannot be identified. In the ex-warehouse process, it is possible to output unqualified products, for which re-inspection should be done, the cost changed, and the corporate credibility damaged. If the storage information is not processed during the warehousing, the efficiency of subsequent inventory management and ex-warehouse management will be reduced, the search of goods rendered difficult, and safety of stocks affected. If the storage information is not confirmed during the warehousing, the inventory update and counting of the received goods will be affected, and inconsistencies between the actual number and the account incurred. Ex-warehouse information: Before goods leave the warehouse, the ex-warehouse form should be received first, and corresponding service truck designated. The form should be then issued to the warehouse keeper and other related personnel. The warehouse keeper should identify the picking route according to the breakdown that indicates the location of goods in the warehouse, and then pick the goods and update the inventory records. After goods leave the warehouse and are removed off the shelf, the warehouse keeper should drive the truck full of goods to the door and check whether all the goods are consistent with the ex-warehouse form. After verification, they will be sent out of the warehouse and recorded in real time based on the fed back information. If there is a problem with the shelf information, the efficiency of picking will be reduced and the failure of delivery from storage caused. If there is a problem with the ex-warehouse form in terms of quantity and specification, the accuracy of the goods sent from the warehouse will be affected. If the storage information is not confirmed during the delivery, the inventory update and the counting of outgoing goods will be affected, and inconsistencies between the actual and the account incurred. Inventory information: It is used to reasonably arrange the order of placing goods in the warehouse and keep the warehouse area tidy. The inventory quantity and quality should be checked on a regular basis, the storage area of the goods in the warehouse clarified, the demand changes in this area mastered according to the past demand, and the order point and quantity identified according to the changes, so as to ensure a reasonable inventory level. Production should not be affected due to insufficient inventory, increase in cost due to excessive inventory be avoided, and production and delivery from storage not be affected due to product safety issues. When leaving and entering the warehouse, the products should be picked according to the warehouse-in and ex-warehouse forms. Special attention should be paid to the specifications and quantities of the products, and the inventory updated in time. Errors in the picking process will affect the delivery of goods and the subsequent logistics activities. For real-time replenishment, it is related to the calculation of quantity. The delay and uncertainty of information in this link will cause tremendous changes in the inventory of goods and thus affect the production and increase the

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cost for the enterprise. Location management in the warehouse should be clearly marked. The location for corresponding products should be inquired before they enter the warehouse, so as to leave the space for them and improve the utilization rate of the warehouse. The dynamic information about inventory should be mastered in time, reduce the occupied space and accelerate the turnover of funds. Excessive inventory requires an increase in the area of warehouse area, higher storage costs, and occupation of a lot of funds, thereby affecting the time value and opportunity benefits of funds with resources put in disuse and their rational allocation not optimized. Too small an inventory will lead to a drop in the service level and failure of meeting the demand in time in case of emergencies. (3)

(4)

Loading and unloading information. The loading and unloading operation involves loading and unloading, handling and moving, stacking and unstacking, and sorting and distribution. Loading and unloading activities run through the entire logistics activities and are the key to the logistics speed. Loading, unloading and handling rely on labor and equipment. If there are too many unnecessary repetitive loading and unloading activities, a lot of expenses on labor and machines will be incurred. For example, the production of 1t of finished products in a machinery factory requires 250 times of loading, unloading and handling, and about 15% of the total cost. Loading, unloading and handling consume a high proportion of cost and time. The high frequency of loading and unloading will lead to a high rate of repetition, the waste of time and the reduction of logistics efficiency that is mainly manifested in transportation efficiency and warehousing efficiency. During short-distance transportation, the time taken for delivering, loading, unloading, and warehousing goods sometimes even exceeds that for their transportation. If loading, unloading and handling takes up too much time pointlessly, the frequency of transportation and the speed of cargo turnover will be affected. Most of the time is wasted on loading, unloading and handling, and the overall logistics operation rendered inefficient. Also, because the goods will inevitably be subject to external forces during the loading and unloading, such as impact and squeezing, they will be damaged and costs increased [35]. For example, pressurized spherical tanks with liquefied petroleum gas are prone to damage during loading and unloading, glass products tend to be broken during the handling, and dangerous goods such as fireworks and other flammables and explosives may explode if improperly loaded and unloaded. It can be seen that loading and unloading is an important part to affect logistics efficiency. The efficiency of loading and unloading, as an important link in logistics, will directly affect other links. Unreasonable loading will affect the transportation of the goods, and damage is likely to occur. Improper unloading will affect the subsequent flow of goods [36]. Therefore, reasonable and effective standardization of loading, unloading and handling helps reduce the time spent, ensure the quality of products, and improve the timeliness and reliability of the system. Packaging and processing information. Packaging and processing play a role in protecting products, facilitating storage and transportation, and promoting

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sales in the circulation process. Reasonable packaging and processing can protect products from natural invasions such as sunlight, rain, and dust, prevent losses caused by volatilization, leakage, melting, pollution, collision, extrusion, loss, and theft, and facilitate storage, transportation, dispatch and sales during the circulation, such as loading and unloading, stock counting, palletizing, shipping, receiving, transhipment, and sales counting. The packaging and processing link is important in logistics. It is necessary to remove the hidden safety hazards in this link immediately, and fortify safety supervision. The source of raw materials and various ingredients should be recorded on the production line, so that the products are placed under strict supervision, control and management in each step of the processing link. Its information also plays an important role to the reliability of the entire logistics system. For example, in the processing of vegetables, the pre-cooling time, temperature, and work flow should be found out by reading the information from the electronic tag of each kind. With these basic data can vegetables be processed. With its absence, temperature control may be neglected during the processing, and the quality of the product will be affected. Many potential safety hazards may occur during processing, so it is necessary to increase the supervision and control in every step. For example, information about additives and their names, places of origin, quantities, and time of addition will be provided for consumers for reference. The information about the processing manufacturer, processor, administrator, and the time when the processing is completed helps to locate the responsible person and hold him or her accountable when problems arise. Without it, it is impossible to clarify which part led to the safety related problem as it occurs. Information about packaging specifications and identification is helpful as guide for stacking and storage of products. Without it, quality problems may occur during the handling and storage of products. For example, upper and lower positions and fragile information are indicated on the packages for some products. If damage occurs during packaging or processing or packaging is not uniform, the goods tend to be damaged during the subsequent handling and transportation, so as to reduce the logistics efficiency and weaken the reliability of the entire system. With the information fully recorded during the processing, the basic information about the product can be checked in following procedures, and time saved. More important, all stages of logistics will become more efficient, more intelligent, more transparent, safer and more reliable. The logistics system serves as communication among suppliers, distribution centers, and customers. The information as the medium can replace inventory according to the distribution plan. For example, Dell has followed a model of replacing inventory with information. In theory, Dell organizes production based on customer orders, which means that there is no labor or material in its factory and workshop before the customer places an order. The distribution information system generally sets the information flowing throughout the distribution system with such functions as collection, transmission, storage, processing, and output. Errors in the information system will affect the distribution network across the city.

4.2 Analysis of Influencing Factors

4.2.1.2 (1)

(2) (3)

(4)

(5)

99

Evaluation Indicators

Information-based operation rate. It refers to the proportion of the volume of logistics done with information technology to the total in the urban logistics system. Logistics information technology is applied to all aspects of logistics and an indicator to measure the benefits of logistics information construction. Information-based application rate. It refers to the proportion of time with information technology applied to the total duration of logistics. Information-based investment rate. It refers to the proportion of investment in logistics information infrastructure construction to total investment in logistics infrastructure construction, and reflects the quantity of investment in the construction of logistics information infrastructure. The proportion of information technology personnel. It indicates the ratio of professional and technical personnel in information technology to the total number of people in the urban logistics system, and is an indicator to reflect the capacity of logistics information. Talents in logistics information technology are an important factor affecting the success of logistics information construction, and include professionals in network communications, computer applications, software development, website building, and logistics information system security [37]. Utilization rate of information platform. It refers to the ratio of the transaction volumes on various logistics information platforms, logistics park information platforms, and electronic port platforms to the total volume of transactions in logistics.

4.2.2 Operational Capabilities 4.2.2.1

Mechanism of Action

The urban logistics network has the structural characteristics of a complex network, including a large number of logistics nodes, scattered distribution in space, and a complex and changeable road traffic environment. The logistics nodes in the city are abstracted into nodes of a complex network, the starting and ending points of logistics into OD pairs of the complex network, and routes in the city into the edges of the complex network. That is, the factors of logistics operation can be abstracted into a complex network for the study of nodes and routes [38]. The spatial structure of urban logistics is composed of points, lines and planes. The point refers to a node in the urban logistics as the most basic power source for the structure of the urban logistics system, such as a logistics center, warehouse and port. The line refers to a route to connect nodes, such as a railway Line and a highway. The plane refers to a logistics area in the spatial structure of urban logistics [39]. Within the node of distribution center, man or machine is required for delivery, unpacking, inspection, packaging, assembly, processing and storage of the goods

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before they are sent to the next node. Errors in the process will lead to delivery of defective products to customers, and are related to stocking, storage and the quality of delivery personnel specifically. (1) Stocking is the basic of distribution and involves ordering, purchase, collection, and replenishment. Stocking is the key to delivery and an important factor that affects the reliability of the urban logistics system. The delivery of goods will be delayed due to errors in manual information input, labeling, and scanning. (2) Storage is a guarantee for meeting customer needs and responding to emergencies, and an important part of the reliability of the urban logistics system. Storage should be carried out in a planned and periodic manner based on past experience and customer orders. Too large a storage volume will lead to an increase in the storage cost, a longer cycle of capital operation, and even certain risks for those updated much more frequently. (3) The quality of the distribution personnel directly affects the delivery rate of goods and customer satisfaction. The requirements for industry management of the distribution personnel include those for timeliness (is it delivered within the specified time or overtime), safety (is it damaged or lost), and customer satisfaction (filling in the waybill and whether the packaging is complete in terms of operation). In addition to the distribution center nodes, problems on the link will also affect the delivery results. The size of the transportation capacity, the selection of routes, and the dispatch of delivery vehicles will determine the delivery time, and thus affect the reliability of the entire distribution network. Corresponding to urban logistics functions, urban logistics channels are divided into that within the city and logistics channel to the outside. The channel in the city mainly serves its own needs and its industry, commerce, and people distribution. The urban roads and nodes are vehicles of the channel inside the city. The channel to the outside plays the role in communicating with surrounding areas. The ports, railways, and warehouses in the city are all nodes that make up it. In Urban Road Traffic Planning and Design Specification (GB50220-95), urban roads are divided into four grades, namely, express roads, main roads, secondary roads and branch roads. Urban express roads and main roads constitute the backbone of the urban road network and connect the main districts of the city. The urban secondary roads work with the main roads and play a role for distribution. Urban branch roads make up for the deficiency of the main roads and function to serve. According to the level of urban roads, logistics channels in the city can be divided into four types: logistics channels to the outside, fast freight channels in the city, fast distribution channels in the city, and terminal distribution channels in the city. Urban logistics channels to the outside are transportation routes across the city and serve to link the city with surrounding areas. The urban express freight channel is a line connecting the nodes in the city and plays the role in linking the clusters within the city. The urban express delivery channel plays the role in connecting the nodes at different levels in the city. The terminal distribution channel in the city functions for delivery at the city end. Each channel plays its own role to facilitate the logistics through nodes, cities and surrounding areas. The channel inside the city with a mixed structure serves to delivery goods entering the city and in the city to each node of the city and to terminal customers.

4.2 Analysis of Influencing Factors

4.2.2.2 (1)

(2)

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Evaluation Indicator [38]

Density of nodes. The density of nodes is the number of nodes per unit area in the area to be evaluated, and reflects the reliability of the network. The higher the node density is, the lower its vulnerability and the higher the reliability of the system is. It is to evaluate the ability of the network to maintain its original function when the nodes and links in the network fail. Accessibility of nodes. The accessibility of nodes refers to the length of the travel distance from a certain node to any other node in the area to be evaluated. The smaller the accessibility is, the higher the connectivity of the network is and the more reliable the system is. It evaluates the convenience of the network. If the average distance Di from a certain node i to any other node is used to evaluate the accessibility of the network, the formula is as follows: N Di =

(3)

j=1, j=i

(5)

(6)

( j = 1, 2, . . . , N )

(4.1)

where N represents the total number of nodes in the area to be evaluated, Di j the average shortest travel distance from node i to any other node j, and M the number of accessible nodes in the area to be evaluated, which is calculated by subtracting the number of inaccessible nodes from the total number of nodes. Availability of road sections. Availability of road sections is the ability to evaluate whether the carrying capacity of a certain section in the system can meet the actual requirements for traffic flow. The higher the availability of the road section is, the more stable the system is and the higher its reliability is. ra =

(4)

M

Di j

μa − χa , ∀a, χa ≤ μa μa

(4.2)

When χa ≥ μa , the actual traffic volume of the section a is greater than its carrying capacity, and the section is unavailable. μa is the maximum carrying capacity of the section a (a ∈ A): χa is the actual traffic flow on the section a (a ∈ A). Line efficiency. The line efficiency is the ratio of the shortest planned travel time between the origin and destination to the actual travel time. It is to evaluate the difference between the actual traffic efficiency between the starting and ending points and the ideal value. The closer the line efficiency is to 1, the closer the actual condition of the line is to the ideal, and the higher the reliability of the system is. Link density. Link density is the number of kilometres of transportation lines per unit area in the area to be evaluated. The higher the link density is, the more reliable the system. Network connectivity coefficient. It is the connectivity of each connected line in the network, and the reciprocal of the product of the weighted average of the shortest distance in each connected line and the number of connected lines. The

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smaller the average shortest distance between any two nodes in the connected line is, the better the connectivity of the system is and the higher the reliability of the network is. The formula for the network connectivity coefficient is as follows: C=

w

1 W

Ni i=1 N

Li

(4.3)

where W is the number of connected lines in the network, Ni the number of nodes in the ith connected line, N the total number of nodes in the entire network, and L i the average shortest distance in the ith connected line.

4.2.3 Reliability of Technical Equipment Technical equipment for logistics is a complex technical support element that runs through the entire logistics process and involves the details of operation in the logistics system. It is the material basis for logistics and a key factor in logistics operation efficiency [40].

4.2.3.1

Mechanism of Action

With the development of urban distribution, modern technology is increasingly used in distribution. Facilities and technical equipment are an important element of the logistics system, undertake multiple tasks in all aspects of logistics operation, and occupy a very important position in the logistics system. Logistics involves packaging, transportation, loading and unloading, handling, storage, circulation and processing. The efficient and stable operation requires logistics technology and equipment as the foundation. Logistics facilities and equipment include those for logistics packaging, warehouses, warehousing, transportation, loading and unloading, handling, container unitization, port logistics, and circulation and processing. In the face of multiple types of goods, orders in small and multiple batches, short duration of delivery, and urban road congestion, modern technology can help convey freight information and reduce costs. Advanced logistics facilities and equipment are the guarantee for efficient, high-quality, and low-cost operation of the entire logistics process. Without the logistics equipment, the operation efficiency of the logistics and distribution system will be extremely low or even broken down. In recent years, governments at all levels in China have attached great importance to the logistics industry, which has pushed up the overall number of logistics equipment rapidly, such as loading, unloading and handling equipment, transportation equipment, warehousing equipment, and packaging equipment. The automation and information technology of logistics equipment have been developed to a certain extent

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[40]. Logistics equipment has been used in various links such as warehousing, transportation, packaging and processing. Foreign companies into China have provided high-performance technical equipment. Chinese companies have constantly developed new products, including new specialized logistics equipment [40]. They have provided a guarantee for the reliability of logistics. For example, in recent years, high-tech manufacturing companies and tobacco companies have built high-level logistics systems, so as to effectively ensure the efficient operation of the logistics system and improve the reliability of the entire system. With the development of production and technological progress, advanced technology and equipment have been continuously applied to logistics in many aspects. A complete logistics system cannot do without the support of modern technology and equipment. For example, the wide application of pallets and containers has improved circulation efficiency and reduced social logistics costs. The automated three-dimensional warehousing system and AGVS have promoted the automation of the handling and shipping system and efficiency of logistics. The urban logistics and distribution process has many links, such as orders, scheduling, warehousing, transportation, and delivery. Each link is closely related to others, and the delay in any link will affect the following links and hence the entire task. For example, there are many problems in the logistics for commercial vehicles. The transportation of commercial vehicles from the logistics base to the railway station will inevitably encounter traffic jams or traffic accidents, which brings certain challenges to the efficiency of transportation. If the commercial vehicle cannot arrive at the railway station in time, and the logistics base is not notified in time, the scheduling task will be seriously hindered. Likewise, the commercial vehicles should be handed over in terms of quality during the short-distance transfer, during which the quality of commercial vehicles is most likely to be damaged and monitoring and recording are needed. When the commercial vehicles are delivered to the railway platform in a short distance, they should be stored and wait for shipment. Before shipment, they should go through several inspection procedures. First and the most important, the destination information should be checked. Short-distance drivers may park the vehicles in the wrong area and confuse the destination. During the transportation, enterprises or customers can only check the specific information by calling the driver. It is not only a waste of time, but also causes a delay. As the intransit information cannot be obtained in time, the company cannot monitor basic information such as the driving status and location. If an accident occurs, the company cannot take control over the vehicles and will find it far from flexible to reorganize for the transportation. The transportation of commercial vehicles cannot be effectively guaranteed in terms of quality and time, which will adversely affect the service level of the enterprise as well as its competitiveness [41]. The many problems in the transportation process should be solved with the support of logistics technology and equipment, so that the losses caused to the enterprise by the delay in delivery can be made up, and the reliability of the entire system improved. As shown in Fig. 4.6, the logistics monitoring system helps to improve the reliability of the system. The system can reflect the information about the vehicle’s

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4 Factors Influencing the Reliability of Urban Logistics System

Fig. 4.6 Logistics monitoring system

latitude and longitude, speed and flow direction in real time and a straightforward manner, and dispatchers can start to track the location of the vehicle at any time. The optimal route and scheduling information will be sent to the vehicle terminal through the GPRS system. The driver can use GPS for navigation through the vehicle terminal. The monitoring center can contact the driver at any time through the voice call and the camera to ensure his or her safe driving. The basic information about the goods will be stored in the electronic label, and the information platform can check it at any time. During the transportation of the vehicle, the company uses RFID technology to identify and process the information about the goods in transit, transmits the relevant information back to the monitoring center through GPRS, and compares the information sent back with that in the database and analyzes them to identify if the driver in transportation makes any violations and if the number of vehicles is right. In the event of an emergency, the driver can press the alarm button to ask the platform for help. The alarm will be started and the alarm information and the current location of the vehicle displayed on the LED display. The GPRS communication module will feed the information back to the monitoring platform for the staff to deal with it immediately [42]. Logistics technology and equipment play a supporting role in the logistics operation, despite that they are also prominent problems affecting the urban logistics industry in China. At present, technology such as bar code, RFID, GPS, and GIS have been used in corporate management and logistics, and is critical to improving operational efficiency and reliability of the logistics system. However, the technical equipment in the logistics industry in China sees insufficient innovation, and the technical facilities on the whole cannot meet the needs of enterprises and the market for efficient operation. GPS has large coverage and high accuracy. When vehicles travel in mountainous areas, tunnels or other similar areas, however, the satellite signals will be reduced, errors occur, and continuity lowered. With the passage of time, the errors of the

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heading sensor and the position sensor of DR will cause an accumulation of errors in positioning, which will prevent the enterprise from getting the information about the vehicles in transit in real time and may thus affect the timeliness of delivery and the reliability of scheduling. The management of parts bins and racks is a practical and complex problem encountered during the logistics operation. For example, to assemble a car requires approximately 30,000 parts, and a large automobile manufacturer needs approximately 1.5 million racks in its global automobile production system. As the output and number of products and production areas continue to rise, problems have arisen in the management of bins and racks. For example, there can be both excessive and insufficient stocks, bins and racks may be damaged or see a high loss rate, losses occur frequently, information is not tracked, and the bins and racks may not be properly arranged at various nodes. It is difficult to predict the demand for bins and racks, and the ability to respond to demand is weak. It is difficult to obtain the turnover rate of bins and racks, or increase the utilization rate, or find out the reasons for damage or loss of bins and racks, or propose effective protective measures. Bins and racks can help significantly improve the loading and unloading effect, and increase the turnover speed, so their damage will directly weaken the quality of the product and the turnover speed, hinder the following links, and then reduce the operational efficiency of the entire urban logistics system. China just started with the development of logistics equipment. There are no unified standards for logistics technology and equipment, so logistics activities are loosely linked and the reliability of the entire logistics system weakened. The high-tech equipment is costly, enterprises go after profits, and many SMEs are unwilling to throw money, so it is difficult to popularize technical equipment. Also, enterprises lack sufficient knowledge of logistics technology and equipment or the concept of integration or overall outlook on equipment selection, so the technical equipment cannot serve logistics operation well. The delay in equipment procurement, unqualified equipment and other factors will also bring hidden dangers to the reliability of the urban distribution network.

4.2.3.2 (1) (2)

Evaluation Indicator

Rate of input in technical equipment. It refers to the ratio of the amount of funds invested in the purchase of technical equipment to the total funds. Proportion of advanced technology and equipment. It reflects the proportion of the number of logistics technology and equipment at the international level. Proportion of advanced equipment Utilize the quantity designed in 1990s internationally × 100% = The total amount

(4.4)

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4 Factors Influencing the Reliability of Urban Logistics System

(3)

Failure rate of technical equipment. It reflects the reliability of technical equipment, and the reliability of technical equipment will directly affect the operational efficiency of the entire system. Failure rate per unit time =

(4)

(4.5)

The degree of utilization of technical equipment. Too low a utilization rate of technical equipment will lead to a waste of logistics resources, and also increase the investment in other fields such as manpower. Technical equipment utilization =

(5)

Number of failures × 100% day

Actual use time × 100% (4.6) Logistics operation time

Rate of investment in R&D and innovation. It is the proportion of investment in technology and equipment R&D in the system to the total funds. The innovation of technical equipment helps improve the efficiency of logistics operation, which is the vitality of the system [43].

4.2.4 Policies and Regulations 4.2.4.1

Mechanism of Action

With the development of the logistics industry in China, great changes have taken place in the policy environment. Distribution companies have no say in government policies and regulations, and changes in them will exert a significant impact on the development of the logistics industry. According to statistics from the China Federation of Logistics and Purchasing, more than 30 provinces, municipalities, and autonomous regions across China have published logistics development plans and necessary industrial policies. With policy support, logistics enterprises and enterprise logistics have embarked on the fast lane [44]. (1)

Infrastructure supply policy. Logistics infrastructure is the basic condition necessary to develop modern logistics, mainly including railways, highways, inland waterways, ports, transportation tools, and storage facilities. Logistics infrastructure policies target the construction of logistics infrastructure, including those for the construction and layout of logistics infrastructure, the system to encourage diversified investment in logistics infrastructure, and specific incentive policies for investment. To ensure that the logistics infrastructure can adapt to the ever-increasing and changing logistics needs, the Chinese government has formulated a series of policies to regulate and promote the construction and layout of logistics infrastructure. The Tianjin Logistics Industry Adjustment and Revitalization Plan published in 2009 proposes to establish a logistics project reserve library, strengthen the planning and layout

4.2 Analysis of Influencing Factors

(2)

107

of logistics parks, and build logistics parks oriented on freight service, production service, commercial service, international trade service and integrated service. The Notice of the State Council on Printing and Distributing Comprehensive Transportation System Planning in the “Twelfth Five-Year Plan” Period published in 2012 proposes to “comprehensively promote the construction of comprehensive transportation hubs, and build 42 national comprehensive transportation hubs.” It has played an important role in improving the overall efficiency and service level of transportation, reducing logistics costs, and solving problems such as the poor connection between comprehensive transportation modes at this stage in China. The National Logistics Park Development Plan (2013–2020) clarifies the development goals and overall layout of the national logistics parks, and draws a “road map” for the development of the logistics parks, so as to solve the problems concerning the lag, difficulty of implementation and high price in land use for logistics infrastructure. Logistics infrastructure is the basis for supporting logistics operation and a prerequisite for ensuring the sound development of the logistics industry. The government uses policies and regulations to regulate the investment, construction, layout, use, management, and maintenance of logistics infrastructure, and attract and encourage private investment in logistics infrastructure, so as to ensure that more logistics infrastructure are built with improved functions, more reasonable layout and higher use efficiency. Management policy. China has listed the logistics industry as one of the “Top Ten Sunrise Industries” and has continuously introduced policies to promote its development and guide its sound development. In 2001, modern logistics was included for the first time in the outline of the Five-Year Plan for National Economic and Social Development. In the same year, the former State Economic and Trade Commission and others jointly issued the Several Opinions on Accelerating the Development of Modern Logistics. Since 2010, logistics support policies have been constantly published, especially after the launch of the logistics industry revitalization plan in 2009. The importance attached to logistics has been elevated to a new height. The Logistics Industry Adjustment and Revitalization Implementation Plan of Beijing published in 2010 proposes to establish the logistics industry as a pillar and build Beijing into a central city in logistics with regional and international influence. In 2011, the General Office of the State Council introduced the Eight Measures and Nine Measures for logistics. In 2012, the Opinions on Deepening the Reform of the Circulation System and Accelerating the Development of the Circulation Industry was passed at the executive meeting of the State Council. The local government has also issued a series of policies to support the development of the logistics industry, such as the Outline of the Twelfth Five-Year Plan for National Economic and Social Development of Shanghai by Shanghai, About Promoting Modern Logistics in the Yangtze River Delta by Shanghai, Zhejiang and Shanghai, Opinions on Implementing Policies and Measures to Promote the Sound Development of the Logistics Industry issued by the General Office of the Beijing Municipal People’s Government in 2012, and Implementation

108

(3)

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Opinions on Promoting the Development of the Logistics Industry issued by the Tianjin Administration for Industry and Commerce in 2012. These policies provide a better platform for the development of the logistics industry. These management and inducing policies provide a vigorous platform for the development of the logistics industry, optimize the development environment for the logistics industry, and guide the sound and orderly competition among and development of enterprises. Economic policy. The macro-control policies of China changed from “maintaining growth, adjusting structure, and preventing inflation” in 2012 to “stabilizing growth, preventing inflation, adjusting structure, and benefiting people” in 2013. China has launched proactive fiscal policies, increased support for major projects, further brought to play the guiding and leading role of government investment, given priority to projects under construction, and launched several major projects in the “Twelfth Five-Year Plan” period in an orderly manner. The government has increased its support for major projects and built a good environment for the development of the logistics industry. China has implemented and improved fiscal and taxation support policies, accelerated the development of strategic emerging industries and service industries, directed consumption toward popular fields, facilitated the consumption of durable goods such as home appliances, household appliances, and automobiles, expanded brand consumption, promoted the development of specialty stores and discount stores, sped up the development of modern circulation, and ensured the stable operation of the market. The logistics industry provides support for the commercial circulation industry. The government has published policies to expand domestic demand, which stimulates greater demand for the development of logistics and advances the development and innovation of the logistics industry. However, the increasing demand is further out of sync with the existing service level of the logistics industry, a challenge to the development of the logistics industry in China.

As an emerging industry, modern logistics needs to rely on a good policy environment. The existing policies and regulations of China have played a certain role in promoting the development of logistics, but there were still constraints regarding the implementation of policies and failure of tallying with the actual situation. Therefore, the policies were not carried out properly as expected [45]. In the first half of 2012, according to the implementation of the “Nine Measures” for logistics by China Federation of Logistics and Purchasing, there was still a large gap from the documents of the State Council and the policy environment required by logistics companies: Enterprises (Shanghai) for the pilot projects of changing from business tax to value-added tax had to pay more tax, the policies to halve land use tax were not sufficiently implemented, road and bridge tolls were high, and enterprises faced many difficulties in mergers and reorganizations and establishment of branches. Therefore, many policies needed to be refined and improved. Policies and regulations issued by the government will restrict the delivery and cargoes, thereby affecting the reliability of urban distribution. For example, the

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government imposes restrictions on the plate number, quantity, time or location of delivery vehicles, and delivery process, which affects the reliability of urban delivery. For various reasons, more cities in China have introduced regulations to restrict trucks travelling in downtown. For example, Beijing requires trucks to only travel in the urban area between 23:00 and 6:00 the next day. Guangzhou delineates the restricted areas in the four directions for trucks with a local license plate and a weight of more than 5 tons or with no local license plate and a weight of over 0.6 tons between 7:00 and 22:00, and all trucks during the rush hours (7:00–9:00, 17:00–20:00). Zhengzhou prohibits the entry of pure electric light-duty vehicles and mini-trucks into areas south of the North Third Ring Road, east of the West Third Ring Road, north of the South Third Ring Road, and west of the East Third Ring Road between 7:00 and 9:00 and between 17:30 and 20:00. Due to restrictions on trucks, buses are often used as logistics vehicles in the city. According to estimates, a truck with a carrying capacity of 2 tons can complete the task for at least 3 light buses. Such measures may increase the pressure on urban traffic, and affect the timeliness and reliability of logistics because some trucks cannot enter the urban area at certain time. Urban land resources are limited, and it is difficult to meet the land demand for logistics The government needs to make planning and approval for land use. The logistics operation system features a large scale, numerous locations, high degree of distribution, complexity in links, diversity in services, and variability. On the one hand, land for logistics enterprise sees lower investment intensity than that for real estate and commercial land. On the other hand, logistics enterprise has outsourced certain business to minimize the operating cost of the entire network and thus paid less tax to local government. For that reason, the local government is not so motivated in providing land for the logistics enterprise, which has hindered the further development of modern logistics. (4)

Tax issues. At present, logistics shoulders excessive tax burden in some aspects. For example, warehousing is far less profitable than transportation and distribution, and sees higher tax as required by the government [46]. In addition, business tax tends to be collected repeatedly, especially in the fields of transportation, distribution, storage and leasing. In order to solve these problems, the State Taxation Administration announced the Plan for Pilot Project of Changing from Business Tax to Value-added Tax and relevant policies for its implementation in Shanghai, which have clarified the value-added tax rate for the transportation industry and some modern service industries. However, due to the different interpretation of the documents on taxation management for logistics in different regions, deviations may occur during the implementation. In addition, different regions have different views on local economic development, so local taxes and their management as well as the implementation of tax policies for logistics differ to a certain extent. The pilot reforms in some areas did not bring more profits to the enterprises. Instead, inconvenience was incurred to their operation because detailed rules were not properly carried out and the calculation of tax rate complicated.

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4 Factors Influencing the Reliability of Urban Logistics System

Since the conferences were held in Copenhagen, countries and regions across the world have proposed the environmental protection, green, and low-carbon concepts. China has promised to reduce carbon emissions. For example, the Opinions on Accelerating the Development of Energy Conservation and Environmental Protection Industries (2013) issued by the State Council, the 2014–2015 Action Plan for Energy Conservation, Emission Reduction and Low Carbon Development issued by the General Office of the State Council (2014), and the Comprehensive Work Plan for Energy Conservation and Emission Reduction in the “13th Five-Year Plan” Period issued by the State Council have effectively fulfilled the commitments made by the Chinese government at the policy level. In response to climate change, the Chinese government promised to reduce carbon dioxide emissions per unit of GDP by 40–45% by 2020 compared with 2005. As an important sector of the service industry, logistics must also take a low-carbon road, the inevitable course for its sustainable development in the future. In the process of developing low-carbon green logistics, the logistics industry will inevitably carry out technical equipment updates and changes in operation, such as the modification of old vehicles in emissions, the purchase of new trucks that meet the emission standards, the use of mechanization, pallet joint operation, unitized stacking, automatic sorting machinery, barcode recognition, electronic scanning, automated packaging and other logistics technology. The planning and design for logistics solutions help avoid or reduce repeated construction and man-made waste. Automated and mechanized logistics operation helps improve the reliability of the system. But with unreasonable configuration or failure of matching the business level of the enterprise, it will be counterproductive.

4.2.4.2

Evaluation Indicator

Given the different types of government policies, their impact on system reliability can hardly be quantified. The key to evaluation lies in how to unify the many indicators of different units with one evaluation scale. Therefore, the government policies can be evaluated from the three aspects of policy formulation, policy implementation and policy performance. The evaluation can be further divided. The content of policy evaluation is shown in Table 4.2 [47]. (1)

The necessity of policy formulation. According to the current development status, analyze the urgency of policy formulation and implementation, whether

Table 4.2 Description of policy evaluation criteria Evaluation standard

Policy formulation

Policy implementation

Policy performance

Rationality of goal setting; Scientific nature of the formulation process

The ability of the implementation subject; Degree of acceptance by the action object; Process supervision and control

Goal achievement; Group satisfaction

4.2 Analysis of Influencing Factors

(2)

(3)

(4)

(5)

111

the process of policy formulation is based on a reliable reality, whether the process of formulation has been fully demonstrated, and whether it is necessary to formulate policies to ensure the reliability of the logistics system. The degree of recognition of the policy. Whether the policy orientation is in line with the government of policy formulation and implement a behavior, the role of the market, and to what extent the policies are accepted by residents and companies. The ability of the policy implementation subject. The logistics policy implementation subject refers to the maker or implementer of logistics policy, that is, a social public institution that represents the public interest of society, like legislative institution, judicial institution and administrative institution. Whether the division of labor between the policy implementation subjects is reasonable, whether the responsibilities and powers are clear, and how is the communication and coordination between implementation subjects. The effect of policy implementation. To what extent the predetermined goal for the policy is achieved, whether the policy has played an important role in alleviating the problem, and whether the expected target has been achieved. Policy satisfaction. Analyze the satisfaction of government departments, enterprises and residents with the policy after its implementation, and its long-term impact on the development of urban logistics.

Experts are selected to analyze the impact of various indicators on the reliability of the urban logistics system based on their own understanding and judgment and rate the importance of each indicator between [0, 1]. The score is higher for more important indicators. The score for each indicator by each expert is obtained, and then added up and divided by the total number of experts, so as to obtain the weight of each actual indicator.

4.2.5 Force Majeure 4.2.5.1

Mechanism of Action

There are many irresistible forces in urban distribution, including weather conditions, traffic conditions, urban emergencies, natural disasters, wars, and large-scale infectious diseases. Weather conditions refer to extreme weather such as rainstorms, snowstorms, and typhoons, which will affect road traffic and hinder the transportation of goods and hence the distribution and transportation. Sudden traffic accidents such as car crashes and road closures caused by special events will bring inconvenience to urban distribution and affect the reliability of the distribution network. Public emergencies refer to those that occur suddenly and cause or may cause heavy casualties, property losses, ecological environmental damage and serious social harm, and endanger public safety [48]. Public emergencies are complex, destructive, sudden, and continuous. Many sudden natural disasters will interrupt the logistics operation,

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so that the smooth flow of urban logistics and the normal performance of logistics functions cannot be guaranteed. Furthermore, incidents such as collective strikes and riots in cities may occur, blocking the passage of vehicles and aircraft taking off and landing, causing obstacles to the transportation of goods and affecting the reliability of the distribution network. Wars and natural disasters such as earthquakes will bring disastrous consequences to cities and impede urban distribution. Densely populated cities are prone to large-scale infectious diseases. Major public health emergencies such as SARS and COVID-19 in 2020 will severely disrupt urban distribution. (1)

(2)

(3)

(4) (5)

(6)

Risk of supply due to extreme weather. For example, earthquakes may cause faults in urban roads, leading to traffic congestion, damage to or failure of transportation of goods, and delay in the delivery of goods to the sales outlets. In the short term, supply will exceed demand, prices rise sharply and go beyond the purchasing power of residents, and market supply be seriously affected. Considering the impact of strong thunderstorms, transporters will be stranded in the traffic and were unable to deliver the goods to the sales outlets on time. There may be a shortage of supply, triggering panic purchases and abnormal price changes. In addition, due to the impact of snowstorms and sand (dust) storms, visibility on the road will become extremely low, which may cause traffic jams in the city and supply shortages, short supply of reserve commodities at sales outlets, and panic buying. The outbreak on a large scale may result in the malfunctioning of urban transportation. The government may conduct isolation and observation in some areas. Employees in logistics may refuse to enter such areas for fear of being infected, so that the commodities cannot be delivered between the city and the outside, and shortage of goods and social panic will be caused. Severe traffic jams may cause delay in delivery and quality risks. In the short term, it will cause short-term supply at sales outlets in the city, or even insufficient stock, and hence panic buying and abnormal price changes, threaten personal safety of urban residents and lead to social panic. Terrorist incidents may block passage in the city and affect the mentality of logistics personnel, so that goods cannot be transported normally. Strikes may result in insufficient labor in production and distribution, delay in production, insufficient supply, shortage of inventory, and even selling out. Products cannot be delivered normally after production, and supply cannot be assured. Transportation problems may be caused by government policies. For example, certain international conferences may require temporary traffic restrictions. Trucks can only enter the urban area between 23:00 and 6:00 the next day in Beijing. They can exert certain impact on the reliability of intra-city transportation.

For example, most areas in China experienced heavy fog and snow in 2012. The continuous heavy fog and snow in the northeast, northern China, the Yellow River and Huai River basin, the middle and lower reaches of the Yangtze River, and southern China had a great impact on traffic. The logistics industry with heavy dependence

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on traffic was also affected. The temporary closure of many sections on expressways and frequent delays and cancellations of flights slowed down delivery and freight transportation in the logistics industry, resulting in a decrease in logistics volume and an increase in transportation costs. Some small logistics companies were even temporarily closed. Winter is the peak season for the logistics industry according to the past experience. The logistics industry was not as good as before due to the bad weather. The cargo volume in the winter was down 10 to 20% compared with 2011. Trucks could not run on the road, so some goods could only be stored in the warehouse. They would be delivered after the weather improved. Besides, transportation costs rose. Because highways were closed, freight vehicles could only use provincial or national roads, with speed limited due to the weather. Therefore, all transportation links were slowed down, delivery delayed, and transportation costs increased. The continuous haze in Beijing also greatly affected the logistics industry as the flow of many goods was hindered, and significant losses were caused to the express and logistics industries. As expressways were closed in the foggy weather, logistics company’s vehicles had to take national highways or provincial highways, and move slowly, and even park on the roadside when the fog became denser. The logistics companies generally send goods in the early morning or morning, but now for safety considerations, they had to delay it. There was heavy fog for two consecutive days, and the delivery was delay for half a day to about one day. If it continued, a large number of goods would be kept long in stock. Moreover, insufficient goods needed to be delivered and many highways were closed, trucks could not be dispatched normally and materials could not flow properly. Also, it would be difficult to estimate the exact time of delivery. In hazy weather, delay would occur in most cases. It would be difficult to determine when to dispatch the trucks, and for them to arrive on time even already dispatched. In March 2010, the eruption of a volcano in Iceland caused the closure of many airports in Europe. Passengers were stranded, and logistics seriously affected. Volcanic ash not only affected the logistics in Europe, but also some logistics companies in Guangdong Province that rely on air transportation for importing and exporting goods. A logistics company in Guangdong had dozens of tons of goods not delivered in time. Some goods already sent could only be placed in the transfer station due to the volcanic ash, which incurred a lot of economic losses to the enterprise. Well-known logistics companies, such as DHL, Fedex, TNT, and UPS, were affected to varying degrees regarding the aviation business between China and Europe. Some companies stopped receiving packages and others temporarily stored packages in warehouses. The volcanic ash caused overstock and severe challenges to the logistics industry [49]. The outbreak of COVID-19 in early 2020 was a big test for the reliability of urban logistics system. It hit when China greeted the Spring Festival, and the combined influence of multiple factors caused fluctuations in the prices of daily necessities in some cities. In the severely affected Wuhan for example, the increase in vegetable prices and others was due to that in labor and logistics, which tripled than before, according to the announcement at a press conference held by the Hubei Provincial Center for Disease Control and Prevention on February 29, 2020.

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4 Factors Influencing the Reliability of Urban Logistics System

The force majeure in logistics service mainly refers to the risk that cannot be predicted or with no precautionary measures taken due to changes in the external environment, such as natural disasters and man-made disasters, changes in the economic environment, political turmoil and changes in the social environment. The changes in these external factors will affect the enterprises at a certain node during the integration of the logistics service supply chain, and further weaken the stability of the entire supply chain [50]. The difficulty and risk of logistics service will greatly be increased.

4.2.5.2

Evaluation Indicator

The impact of force majeure on the reliability of the urban logistics system is quantified by rating the possibility of force majeure and the severity of the consequences, and an evaluation matrix established. When analyzing its impact on the reliability of the urban logistics system, it is quantified according to the evaluation matrix. (1)

Evaluate the level of possibility of force majeure. The ultimate possibility is assigned with a qualitative and relative grade method. The possibility of force majeure is divided into five levels: A. almost certain to occur; B. very likely to occur; C. likely to occur; D. less likely to occur; E. almost impossible to occur. See Table 4.3 for the definition of specific possibilities at each level.

(2)

Evaluate the level of consequences of force majeure. The consequences of force majeure are divided into four levels. I–IV represent the consequences of the force majeure at four levels. The specific consequences at each level are defined in Table 4.4.

(3)

Confirm the risk level. Based on the possibility and consequence levels of force majeure, its risk levels can be divided. Refer to Table 4.5 for specific risk levels.

4.3 Refined Model of Influencing Factors The matter-element analysis is used to establish an evaluation model for multiindex performance parameters of influencing factors is established, from which the influencing factors are withdrawn.

4.3 Refined Model of Influencing Factors

115

Table 4.3 Possibility level Grade

Logo

Possibility definition

A

Almost certain to occur

• The probability of an 0.9–1 incident is very high, and in most cases it is almost inevitable; • Or it can be confirmed to occur frequently.

Possibility of occurrence

B

Very likely to occur

• The possibility of an incident is high, and it is likely to happen in most cases; • Or it can be confirmed to occur before

0.7–0.9

C

Likely to occur

• The possibility of the incident is medium, and it may happen under certain circumstances; • But it is not confirmed to occur before

0.5–0.7

D

Less likely to occur

• The incident is less likely to 0.3–0.5 happen, and generally unlikely to happen; • It is also not confirmed to occur before

E

Almost impossible to occur

• The incident is almost impossible to happen, only possible in very rare and exceptional circumstances; • It is also not confirmed to occur before

0.1–0.3

Table 4.4 Consequence level Grade Logo

Definition of consequences

I

Particularly significant • The incident, once occurring, will exert a particularly significant impact; • Or it caused a severe impact

II

Significant

• The incident, once occurring, will exert a significant impact; • Or it caused a major impact

III

Big

• The incident, once occurring, will exert a big impact; • Or it caused a great impact

IV

Average

• The incident, once occurring, will exert an average impact; • Or it caused certain impact

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4 Factors Influencing the Reliability of Urban Logistics System

Table 4.5 Reference for risk level Possibility\Consequences Level Probability

I

II

III

IV

Particularly significant

Significant

Big

Average

A

0.9

Extremely high

Extremely high

Extremely high

Extremely high

B

0.7

Extremely high

Extremely high

High

High

C

0.5

Extremely high

High

High

Medium

D

0.3

Extremely high

High

Medium

Low

E

0.1

High

Medium

Low

Low

4.3.1 Matter Element Analysis 4.3.1.1

Theoretical Overview

Matter element analysis was first proposed by Associate Professor Cai Wen, a Chinese scholar. It is an emerging discipline that studies the laws and methods for solving incompatible problems. The development of matter element analysis has aroused widespread attention from researchers at home and abroad. Professor Wang Peizhuang, vice chairman of the Chinese Society of Fuzzy Mathematics, pointed out: “It is a new subject between mathematics and experimental science.” Some scholars also pointed out: “Matter-element analysis is a new subject with great potential and development prospects” [51]. Matter element analysis can transform an incompatible system into a compatible system, and plays an important role in solving the incompatible problem in the system. The qualitative and quantitative changes of things are a unity of opposites. Both must not be ignored. Matter element analysis is a tool to solve problems from both quantitative and qualitative aspects, and organically connects the two [51]. The matter element is a three-element group consisting of things, characteristics, and the value of things with respect to the characteristics, denoted as: R = (Affairs, Feature, Measure = N , c, v) Matter-element analysis can describe the changing process of objective things in a true and appropriate manner, and is an effective tool for solving contradictory problems.

4.3 Refined Model of Influencing Factors

4.3.1.2 (1)

(2)

(3)

(4)

117

Meaning of Matter-Element Analysis Theory

The concept of matter element. Matter element refers to a comprehensive reflection on the relationship between things, characteristics and the specific values of characteristics. The quality and quantity are specifically considered, and the variability of things described when dealing with problems [51]. Three factors of matter element [51]. Things: Things are the main factor of matter element. According to whether they really exist, things can be divided into virtual things and present things. According to their attributes, things can be divided into individual things and grouped things. Characteristics: The characteristics of matter element refer to the nature, function, and state of things. According to the degree of use to solve the problem, the characteristics can be divided into function, nature, and actual meaning. Value: The value of things refers to the quantity, range or degree of a certain characteristic or a certain type of characteristic. There are quantitative and non-quantitative values. Non-quantitative values are descriptive literally; quantitative values are those represented by real numbers and a certain dimension. Characteristic matter element. Characteristic matter element M = (c, v), where C represents characteristic and V value of the characteristic. A thing can have multiple characteristic matter elements, and a certain characteristic matter element is not unique to a certain thing [51]. Multi-dimensional matter element. A thing has multiple characteristics, and if a thing N has n characteristics c1 , c2 , . . . , cn and the corresponding values v1 , v2 , . . . , vn , it is expressed as: ⎡

N c1 ⎢ c2 ⎢ R=⎢ .. ⎣ .

⎤ v1 v2 ⎥ ⎥ .. ⎥ . ⎦

(4.7)

cn vn

Then R is called multi-dimensional matter element, where Ri = (N , ci , vi ), (i = 1, 2, 3, . . . , n) is the partial matter element of R, denoted as: ⎡ ⎤ ⎤ v1 c1 ⎢ v2 ⎥ ⎢ c2 ⎥ ⎢ ⎥ ⎢ ⎥ C = ⎢ . ⎥, V = ⎢ . ⎥ ⎣ .. ⎦ ⎣ .. ⎦ cn vn ⎡

(4.8)

118

4 Factors Influencing the Reliability of Urban Logistics System

Assuming that the influencing factor N has n characteristics c1 , c2 . . . cn , and the corresponding values are v1 , v2 . . . vn , then the corresponding matter element matrix is: ⎤ ⎡ ⎤ ⎡ R1 N c1 v1 (4.9) R = ⎣ ... ...⎦ = ⎣...⎦ cn vn Rn Nj represents the divided jth evaluation level (j = 1,2…m), ci the characteristics of Nj (i = 1,2…n), Vij the value range of ci corresponding to Nj , and vij = (aij , bij ), and the classic domain is expressed as: ⎡

⎤ ⎡ ⎤ Nj c1 v1j Nj c1 (a1j , b1j ) Rj = ⎣ . . . . . . ⎦ = ⎣ . . . ... ⎦ cn vn j cn (anj , bnj )

(4.10)

Np represents the entire evaluation level, vip is the value range of Np with respect to cj , and vip = (aip , bip ), and the extensional domain is expressed as: ⎤ ⎤ ⎡ Np c1 v1p Np c1 (a1p , b1p ) Rp = ⎣ ... ... ⎦ = ⎣ ... ... ⎦ cn vnp cn (anp , bnp ) ⎡

(4.11)

4.3.2 Gray Correlation Calculation In cybernetics, people often express the clarity of information with the shades of colors. Ashby calls objects with unknown internal information “black boxes”, with “black” to indicate unknown information, “white” to indicate completely clear information, and “gray” to indicate partly clear and partly unclear information. Accordingly, some systems with unclear information are called “gray systems” [52]. In the 1980s, Chinese scholar Deng Julong published the first paper on “Gray Control System” in Chinese, which marked the birth of gray system theory. The publication of the article has aroused the attention from experts and scholars at home and abroad, and the research on gray system theory has become conducted. The gray system studies the uncertain system with “small samples” and “poor information” featuring “partly known information and partly unknown information”, so as to achieve an accurate description and understanding of the real world through the generation and development of “partial” known information [53]. Gray system theory has helped solve many complicated problems by quantifying and simplifying them. Gray correlation is an analytical method in the gray system theory, and to seek the

4.3 Refined Model of Influencing Factors

119

numerical relationship between various subsystems or factors in the system with a certain method. Many methods in mathematical statistics require a large amount of data, and there may also be inconsistencies between the results of quantitative analysis and qualitative analysis. Gray correlation analysis judges how closely the two factors are connected based on the similarity of the geometric shape of the sequence curve, does not require a large sample size, and makes up for mathematical statistics [54]. Because the factors that affect the urban logistics system are complex, extensive and not completely clear, gray correlation can be used to analyze the factors influencing the reliability of the urban logistics system despite some cannot be quantified. It can help analyze to what extent various factors can influence the urban logistics reliability, and feasible conclusions can be drawn. Gray correlation analysis features totality, orderliness, asymmetry and nonuniqueness. It studies the distance of a discrete function relative to several other discrete functions. Therefore, the degree of correlation between each factor is not the focus of the study. It is important to consider its totality, that is, the degree of influence of each subsequence on one sequence. It is more widely applied. The sequence of data in the gray correlation analysis cannot be reversed, otherwise the nature of the sequence will change. The relationship between factor A and factor B in gray correlation analysis is not symmetrical, that is, the correlation of factor A to factor B is not equal to that of factor B to factor A. The correlation is related to the amount of data, the original data processing method and other factors [55]. Based on the analysis and refinement of the factors affecting the reliability of the urban logistics system in the previous section, it is found out that the reliability of the urban logistics system can be investigated from five aspects: information, operational capabilities, technical equipment, policies and regulations, and force majeure. However, some of these factors have obvious gray characteristics and need to be quantified. With the correlation analysis method in gray system theory, the degree of influence of various factors on the reliability of urban logistics system is analyzed, main and secondary factors withdrawn, and the mechanism of action of each influencing factor clarified, so as to lay the foundation for the measure of the reliability of urban logistics and optimization of the system. Assume the reference sequence is: Y = {Y(M) |M = 1, 2, . . . ,n} The comparative sequence is: X = {Xi (M) |M = 1, 2, . . . , n} , i = 1, 2, . . . , m Different units of the data are collected and the quantities vary largely, so the data needs to be dimensionless for the sake of comparative analysis and to make the results more scientific and reasonable [56]. The variable dimensionless becomes: Xi (M) =

Xi (M) , M = 1, 2, . . . n, i = 1, 2 . . . m X1 (1)

Mark i (M) = |Y(M) − Xi (M)|, then the gray correlation coefficient is:

(4.12)

120

4 Factors Influencing the Reliability of Urban Logistics System

ξi (M) =

mini minM i (M) + ρmaxi maxM i (M) i (M) + ρmaxi maxM i (M)

(4.13)

mini minM i (M) is the minimum difference between the two levels, which means that the minimum value of i (M) is first taken according to different values of i, and then the minimum value is taken according to the different values of M in mini i (M). maxi maxM i (M) is the maximum difference between the two levels, which means that the maximum value of i (M) is first taken according to different values of i, and then the maximum value is taken from maxi i (M) according to the different values of M [56]. ρ is the resolution coefficient and represents the distortion caused by excessively weakening the maximum absolute difference, ρ ∈ (0, 1), and generally ρ = 0.5 The size of ρ represents the difference between the correlation coefficients, and the correlation coefficient reflects how close the two compared sequences are at a certain moment. The smaller ρ is, the more the difference between correlation coefficients can be increased. After sorting out the correlations, the degree of closeness between the comparison sequence and the reference sequence is obtained, and the main factors affecting the change of the reference sequence are predicted. The formula for the correlation between the comparison sequence and the reference sequence is as follows: ri =

n 1 ξi (M), M = 1, 2 . . . n n M=1

(4.14)

The more larger the value of r is, the more similar the reference sequence and the comparison sequence corresponding to r are.

4.3.3 Weight Determination The weight of each indicator will be determined with the method of entropy weight coefficient. The concept of entropy is derived from thermodynamics. Entropy is a measure of the uncertainty of the system state. When the system may be in n different states and the probability of each state is Pi (i = 1, 2, . . . , n) the entropy of the system is: Pi = 1) (4.15) E =− Pi ln Pi (0 ≤ pi ≤ 1, Assuming that there are m evaluation samples and n evaluation indicators, [Y ] = {y hl } m×n is used to denote the evaluation matrix:

4.3 Refined Model of Influencing Factors

121

⎡

y11 y12 ⎢ y21 y22 ⎢ Y =⎢ . . ⎣ .. .. ym1 ym2

··· ··· .. .

y1n y2n .. .

⎤ ⎥ ⎥ ⎥ ⎦

· · · ymn

(4.16) m×n

According to the nature of entropy, a multi-objective decision-making evaluation model is established. Suppose n evaluation indicators are used to decide and evaluate m samples, yhl is the estimate of the evaluation indicator l in the candidate option h, and yl is the ideal value of the evaluation indicator. The value of yl varies with the characteristics of the evaluation indicator. For profitability indicators, yl should be as large as possible. For profit and loss indicators, yl should be as small as possible [57]. Calculate the proportion of the indicator of the hth item under the lth indicator. The formula for weight calculation is: Ej = −(lnm )−1

n

(yhl lnyhl ), l = 1, 2 . . . n

(4.17)

1 - Ej l=1 (1 − Ej )

(4.18)

l=1

wj = n

where Ej represents the output entropy of the indicator, and wj the weight of the indicator. With the above methods, the degree of the impact of each influencing factor on the reliability of the urban logistics system can be clarified, based on which the key influencing factors can be extracted from the multiple ones. It has laid the foundation for designing the reliability optimization model.

References 1. Zhitai W. “Third-party profit source” and its significance to China’s economic development. China Econ Trade Herald. 2001;6:14–5. 2. Taitian M. Theoretical study on urban logistics planning. Xiangtan: Xiangtan University; 2005. 3. Minxiu L, Kai Z. A brief discussion on the impact of urban logistics in promoting economic development and its countermeasures. Sci-Tech Inf Dev Econ. 2005;15(18):143–4. 4. Bo W. Research on business and logistics development strategy. Beijing: Beijing Jiaotong University; 2007. 5. Guofeng Y. Logistics should become the accelerator of urban economic development. China Logistics Times. 2011;4:32–6. 6. Kai L. Study on the impact of beijing logistics industry on economic development. Beijing: Beijing Jiaotong University; 2010. 7. Wuyi C, Guihe W, Jianguo Lv. System reliability theory. Beijing: China Construction Industry Press; 2010. 8. Jun S, Jianbin G. Promoting role of Beijing logistics industry in urban economic development. China Econ. 2012;9:200–1.

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9. Jing Su. The impact of urban logistics on regional economic development. China Bus Trade. 2011;14:141–2. 10. Yan J. On the importance of modern trade and logistics distribution. Bus Econ Rev. 2002;3:13–5. 11. Yang Y, Shaoyang H. Investigation and research on industrial logistics development status in Beijing. Logistics Mater Handling. 2013;3:118–21. 12. https://www.ndrc.gov.cn/fgsj/tjsj/jjyx/xdwl/201302/t20130227_1182332.html 13. Zhao L. Promote modern logistics management technology and promote the systematic development of industrial logistics. China Market. 2009;5:75–6. 14. Qing M. Supply chain integration and innovation of Beijing Industrial Logistics. Modern Logistics News, 2013-5-7 (B04). 15. Qian Z. A comparative study of urban construction land in shandong province. Jinan: Shandong Construction University; 2010. 16. Xianzhen S. Study on the impact of the highway on regional logistics. Chengdu: Southwest Jiaotong University; 2006. 17. Beijing Municipal Bureau of Statistics. Beijing statistical yearbook 2018. Beijing: China Statistics Press; 2018. 18. Weihua L, Meiying Ge. Manufacturing logistics review of 2010 and outlook of 2011. Truck Logistics. 2011;002:70–1. 19. http://www.spb.gov.cn/xw/dtxx_15079/201601/t20160114_710673.html 20. Biao P. Domestic demand drives people’s livelihood logistics. China logistics and purchasing. 2013;1:32–4. 21. Xianfeng J. Modern logistics activates the competitiveness of Yining City. Xinjiang Daily, 2011-9-3 (7). 22. Min Z, Qinbing Z, Xuejun D. Analysis of the coupling relationship between urban environment and logistics rationalization: take Huizhou as an example. Wuhan: Wuhan University Press; 2010. 23. Yan Z, Jincheng S, Liancheng J. The role of urbanization strategic environment assessment in promoting urban sustainable development. Shanghai Environ Sci. 2007;4:146–50. 24. Zhe Z. Preliminary research on the external uneconomic solutions of urban logistics. Econ Relat Trade. 2011;5:86–8. 25. Guowen W, Wenbo W. Urban logistics: several thoughts on theory and policy. China Open J. 2011;5:10–5. 26. Jianhua Y, Jidong G, Shugang Ma. Environmental impact assessment for urban logistics and distribution systems. Urban Prob. 2012;12:37–41. 27. Xingang W. Urban environment and logistics rationalization. China Bus Mark. 2000;3:10–3. 28. Xiaoxia W. Urban logistics distribution management. Beijing: Tsinghua University Press; 2012. p. 288–9. 29. Munuzuri J, Larraneta J, Onieva L, et al. Solutions applicable by local administrations for urban logistics improvement. Cities. 2004;22(1):15–28. 30. Shao JP, Ma TY, Dong SH, et al. Evaluation and analysis: development trend of China’s logistics industry under supply chain globalization environments. Serv Sci Manage. 2009;2(1):71–9. 31. Kuse H, Endo A, Iwao E. Logistics facility, road network and district planning: establishing comprehensive planning for city logistics. Procedia Soc Behav Sci. 2010;2(3):6251–63. 32. Russo F, Comi A. A classification of city logistics measures and connected impacts. Procedia Soc Behav Sci. 2010;2(3):6355–65. 33. Hao Z. Application of RFID technology in vegetable cold chain logistics management. Logistics Technol. 2013;6 34. Ying X. Distribution management system model and its optimization. Guangzhou: Guangdong University of Technology; 2007. 35. Wei S. Optimization of on-way logistics management for the internet of things. J Changchun Univer Technol (Soc Sci Edn). 2011;23(4):45–6. 36. Yuyi Z, Guiqiang W. Analysis of loading and handling system on military logistics. Logistics Technol. 2005;3:027.

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Chapter 5

Measurement of Reliability of Urban Logistics System

5.1 Reliability Estimation Method Bayesian estimation applies Bayesian theory to parameter estimation and adopts multi-layer Bayesian estimation to evaluate the reliability of urban logistics system. It reduces the dependence of the classical method on the sample capacity of the site test, and makes full use of the samples (such as historical information, expert information, simulation data, etc.) under the same evaluation accuracy requirements, and is more applicable to urban logistics system, and can improve the precise value of prior distribution parameters to a certain extent, thus improving the accuracy of estimation results with a good effect [1]. Assume that lifetime T of the urban logistics system empical sample (no failure operation period) follows exponential distribution and whose density function is: f (t) = λ exp(−tλ), t > 0

(5.1)

Suppose m timing closure test for zero failure data, with closure time of ti , (t1 < t2 · · · < tm ) (unit: month), number of test samples is n i (n i is the sum of the number of individuals eligible for ti in the sample, if the result of the test is that all individuals unfail, (ti , n i )(i = 1, 2, . . . , m) is called zero failure data. If the prior density core of λ is λa−1 , 0 < λ < λ0 , 0 < a < 1 and a is constant but no specific value of a, take the uniform distribution on (0, 1) as the prior distribution of a, and whose density function is π(a) = 1, (0 < a < 1). The prior density of λ is: π(λ|a ) =

a a−1 λ , 0 < λ < λ0 λa0

(5.2)

The multi-layer prior density of λ is:

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. Zhang, Reliability Optimization of Urban Logistics Systems, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-19-0630-5_5

125

126

5 Measurement of Reliability of Urban Logistics System

1 π(λ) =

1 π(λ|a )π(a)da =

0

0

a a−1 λ da, 0 < λ < λ0 λa0

(5.3)

The theorem performs m timing closure test for samples with lifetime obeying the exponential distribution (5.1), and the result is that all individuals unfail and we obtained no failure data (ti , n i ), (i = 1, 2, . . . , m). If the multi-layer prior density π(λ) of λ is given by Formula (5.3), under secondary loss condition, the multi-layer Bayesian estimation of λ is: 1 1 λ= T



0

a(a+1) I (a (T λ0 )a T λ0

1 0

+ 1)da (5.4)

a(a) I (a)da (T λ0 )a T λ0

 x a−1 m 1 and T = i=1 n i ti , Ix (a) = (a) t exp(−t)dt, (0 < x < ∞), is an incomplete  ∞ x−10 exp(−t)dt is the Gamma function. Gamma function, (x) = 0 t Reliability evaluation is an important link based on the macro perspective, from the overall to the local continuous optimization, and is one of the scientific management methods to improve the management level of urban logistics system. For the complex system of urban logistics system, multi-layer Bayesian estimation is more applicable and accurate than the traditional prediction method, monitoring reliability in real time, mastering the statistical characteristics of basic links, which helps to optimize the reliability of urban logistics system [1].

5.2 Reliability Measure Model In the multi-layered urban distribution system, the most common is the dual-layered distribution system. The logistics enterprises mostly choose to build a dual-layered distribution system in urban distribution considering the cost and management efficiency. The reliability of any multi-layered distribution system can be calculated through the reliability of the dual-layered distribution systems. This book builds the reliability measure model of dual-layered distribution system and proposes the reliability measure of urban distribution network. With the reliability of urban distribution network as the overall goal, the following factors will affect it from five aspects: information, operational capacity, technical equipment reliability, policies and regulations, and force majeure. The reliability of each link of the whole dual-layered distribution system is affected by these five aspects.

5.2 Reliability Measure Model

127

Fig. 5.1 Dual-layered distribution system

5.2.1 Reliability of Supplier The supplier’s reliability includes the reliability of the node (supplier) and the connected link (supplier to the primary distribution center route), that is supplier (S1 and S2 ) reliability and link (l1 and l2 ) reliability in Fig. 5.1. The reliability of both nodes and links may be affected by the five factors analyzed in Chap. 4, namely information, operational capabilities, technical equipment, policies and regulations, and force majeure. Taking information factors as an example, the sensitivity and utilization of information will affect their role in the whole system, and then affect the reliability of the whole urban logistics system. Then reliability of the supplier is: R Si =

5  

1 − WkSi

 (5.5)

k=1

and WkSi indicates the probability of failure in the kth indicator of the i-th supplier.

5.2.2 Reliability of Distribution Center and Customer Reliability of the distribution center refers to the overall reliability of the first and second-layered distribution center, including primary distribution center (D1 ) reliability, secondary distribution center (d1 and d2 ) reliability, link road (l3 and l4 ) reliability. Use the customer and its link to the secondary distribution center as a measurement object, namely l5 , C1 , l6 , C2 , l7 , C3 , l8 , C4 .This reliability can be expressed as:

128

5 Measurement of Reliability of Urban Logistics System



 R D · Wd1 · Rd1 WC1 · RC1 + WC2 · RC2 + Wd2 · Rd2 WC3 · RC3 + WC4 · RC4 

5 

 1 − WkD = ⎡

k=1

⎤     5 5 5 

  



 1 − Wkd · WC1 · 1 − WkC + WC2 · 1 − WkC ⎢ Wd1 · ⎥ ⎢ ⎥ k=1 k=1 k=1 ⎢ ⎥ ·⎢ ⎥     5 5 5 ⎢ ⎥   





 ⎣ ⎦ d C C + Wd2 · 1 − Wk · WC3 · 1 − Wk + WC4 · 1 − Wk k=1

k=1

k=1

(5.6) WkD refers to the probability of failure of the k-th index of the first-layered distribution center; Wkd refers to the probability of failure of the k-th index of the second-layered distribution center; WkC refers to the probability of failure of the k-th index of the customer; Wd1 , Wd2 , WC1 , WC2 , WC3 and WC4 are the importance determined by the history of the company’s previous distribution of goods, that is: Wd1 refers to the probability of passing through the second-layered distribution center d1 in all items passing through the first-layered distribution center D; Wd2 refers to the probability of passing through the second-layered distribution center d2 in all items passing through the first-layered distribution center D; WC1 is the probability of passing l4 in all items through the second-layered distribution center, the same as WC2 , WC3 , WC4 .

5.2.3 Reliability of the Distribution Network Model Reliability R can be expressed as: ⎞⎤ ⎡ ⎤ ⎛ ⎡ g 5  5      Si ⎠⎦ ⎣  D D ⎝ ⎣ 1−W 1−W ⎦ R= W · i

i=1

k

k

⎛

k=1

k=1

⎞ ⎛ ⎞ ⎤ 5  5  5        d1 ⎠ ⎝ C1 C2 ⎠ ⎝ ⎥ 1 − Wk · WC1 · 1 − Wk + WC2 · 1 − Wk ⎢ Wd1 · ⎥ ⎢ ⎥ ⎢ k=1 k=1 k=1 ⎢ ·⎢ ⎛ ⎞ ⎛ ⎞⎥ ⎥ 5  5  5     ⎥ ⎢ ⎣ + W · ⎝  1 − W d2 ⎠ · ⎝W ·  1 − W C3 + W ·  1 − W C4 ⎠⎦ d C C ⎡

k

2

k=1

k

3

k=1

(5.7)

k

4

k=1

where g indicates that g suppliers supply to the first-layered distribution center D; WiD is the importance of the i supplier supply to the first-layered distribution center D.

5.3 Influence Degree Analysis

129

5.3 Influence Degree Analysis The reliability measurement of the urban distribution network is to check the past deficiencies and errors, so as to improve the reliability of the network and improve the urban distribution management of enterprises. Introduce the influence degree to analyze the network reliability to identify the links and factors that most affect the reliability of urban distribution network, and improve these links and factors. SkSi = W Si · WkS

(5.8)

SkSi refers to the influence degree of distribution network failure resulting from the k-th indicator failure of the i-th supplier. SkD = W D · WkD

(5.9)

SkD refers to the influence degree of the whole distribution network failure of the k-th index of the first-layered distribution center D. Skdi = Wdi · Wkd

(5.10)

where i refers to the i-th second-layered distribution center, and Skdi refers to the influence degree of the distribution network failure caused by the k-th index failure of the i-th distribution center. SkCi = WCi · WkC

(5.11)

where i refers to the i-th customer, and SkCi refers to the influence degree of the k-th index of failure of the i-th node customer, which caused the failure of the whole distribution network. Therefore, we can know the influence of each specific index of each distribution link on the network reliability. By comparison, we can find out which factor of a specific link has the greatest influence on the reliability of the whole urban distribution network, and thus improve this link and improve the reliability of the urban distribution network.

5.4 Method of Measure Urban logistics system is a composite system with multi-objective properties, so its reliability measure should be a multi-objective comprehensive measurement based on the multi-dimensional space system, as shown in Fig. 5.2.

130

5 Measurement of Reliability of Urban Logistics System

Fig. 5.2 Three-dimensional spatial representation of the system reliability measurement method

5.4.1 Selection Indicators and Principles According to the role of the index in the subsystem and the whole system, the indicators can be divided into: non-key indicators, subsystem key indicators and system key indicators. The indicators selected by the reliability measure of urban logistics system should be the key index of strong independence. At the same time, the selection of indicators should also abide by certain principles: (1)

(2)

(3)

(4)

(5)

Systemic principle. The indicators should be certain logical, which should not only reflect the main characteristics and conditions of the subsystems from different aspects, but also reflect the internal correlation between the subsystems. Each subsystem is reflected by a set of indicators which are both independent and associated to each other to form an organic whole. Typical principle. The selected indicators should be typical, so as to accurately reflect the characteristics of the represented subsystem to the greatest extent, so as to improve the scientificity, standardization and reliability of the results. Dynamics principle. The development changes and mutual influence between some subsystems need to be reflected by the indicators with a certain time scale. Simple and scientific principles. In order to evaluate the scientificity of the results, we must ensure the scientificity of the index selection, which can objectively and truly reflect the characteristics and status of the represented subsystem. At the same time, the selection of indicators should not only prevent being too complex, not convenient for follow-up operation, nor can it be too simple, resulting in the lack of important indicators and affecting the scientificity and reliability of the final results. To master the principle of moderation, and select indicators to be concise. Feasibility principle. It includes three aspects: comparison, quantification, and operability. When selecting indicators, the data of different subjects should be comparable, and the unit of measurement and method of calculation should be the same. All indicators should be as simple as possible and easily accessible to relevant information and data, helping to improve practical operability. In addition, in order to facilitate the subsequent mathematical calculation and

5.4 Method of Measure

(6)

131

statistical analysis steps, quantifiable indicators should be considered when selecting indicators. Comprehensive principle. According to the final purpose of indicator selection, many factors should be taken into account when selecting indicators, and strive that the selected indicators can be more comprehensive analysis and evaluate system, and with no omissions.

5.4.2 Index Standardization By using the technology of fuzzy membership degree to standardize the index, let vi , C(vi ), T (vi ), N (vi ) be the original value, critical value, target value and normalized value of the index i respectively, then Standardization of positive indicators:

N (vi ) =

⎧ ⎪ ⎨ ⎪ ⎩

0

vi −C(vi ) T (vi )−C(vi )

1

vi ≤ C(v i ) C(v i ) < vi ≤ T (vi ) vi > T (vi )

Standardization of reverse indicators: ⎧ ⎪ 1 vi < T (vi ) ⎨ C(vi )−vi N (vi ) = C(vi )−T (vi ) T (v i ) < vi ≤ C(vi ) ⎪ ⎩ 0 vi ≥ C(v i )

(5.12)

(5.13)

5.4.3 Critical Effect Treatment of Reliability in Urban Logistics System When the key index of the logistics subsystem exceeds the critical value, the subsystem of the index will fail. Therefore, the normalized value of all indicators of the subsystem of the index is taken as 0, and other indicators in the system will still be standardized according to the above methods. When the system key index exceeds the critical value, the whole logistics system is in an unreliable operation state, and all the index values are taken as 0.

132

5 Measurement of Reliability of Urban Logistics System

Fig. 5.3 System reliability-weighted space

5.4.4 Determination of Index Weight The weight reflects the importance of a part of the whole on the whole, tending to highlight the impact of the part on the whole. General weight can rely on the classification of hierarchical indicators for identification and calculation, namely the hierarchical analysis method. In addition, fuzzy method, fuzzy hierarchical analysis method and expert evaluation method are also often used in the identification and calculation of weights. Let w1 , w2 and w3 be the weights of x, y and z respectively, then the weighted indexes of system reliability can be transformed into a multidimensional space with the weight of each index as the length of each dimension, as shown in Fig. 5.3.

5.4.5 Measurement of System Reliability Measure the distance of the current system state point and the target point in the multidimensional space to measure the reliability, when the smaller the distance, the better the reliability of the system. Set the reliability of urban logistics system has N indicators, M states, rmn indicates the value of the n-th index in the m-th state, then the system reliability state can be expressed as a matrix: ⎡

r11 ⎢ r21 ⎢ M =⎢ . ⎣ .. rm1

⎤ r12 . . . r1n r22 . . . r2n ⎥ ⎥ .. . . .. ⎥ . . ⎦ . rm2 . . . rmn

(5.14)

After the above normalization and weighting, the indexes are transformed into:

5.4 Method of Measure

133

⎡

y11 y12 ⎢ y21 y22 ⎢ M = ⎢ . . ⎣ .. .. ym1 ym2

... ... .. .

y1n y2n .. .

⎤ ⎥ ⎥ ⎥ ⎦

(5.15)

. . . ymn

Then the critical and target points of the system reliability can be respectively expressed as vectors O1 , O2 : O1 = (0, 0, . . . , 0), O2 = (w1 , w2 , . . . , wn )

(5.16)

where the w1 , w2 , . . . , wn is the weight of n indicators, respectively. The Euclidean distance between any reliability state point I and the reliability critical point O1 and O2 of the system is: ! " " n (yi1 − 0)2 + (yi2 − 0)2 + · · · + (yin − 0)2 = # (yi j )2

I O1 =

(5.17)

j=1

I O2 =

! " " n (yi1 − w1 )2 + (yi2 − w2 )2 + · · · + (yin − wn )2 = # (yi j − w j )2 j=1

(5.18) Therefore, the reliability level of the type i-th state of the urban logistics system is: R=

I O1 I O1 + I O2

(5.19)

Reference 1. Sifeng L, Yaoguo D, Zhigeng F et al. Grey system theory and its applications. Beijing: Science Press; 2010.

Chapter 6

Study of the Reliability Optimization Model for the Urban Logistics System

The optimization goal of urban logistics system is to improve the timeliness and reliability of urban distribution. Due to the complexity and versatility of the urban logistics system, natural disasters, human factors and other emergencies will have an impact on the whole system, and the failure of any link in the system will lead to the collapse of the whole system. Therefore, on the basis of considering the characteristics of multiple varieties and small batch of urban logistics distribution, the reliability optimization study of urban logistics system from the reliability of nodes and lines, which is helpful to establish a deeper and clear reliability optimization model of urban logistics system. The reliability of urban logistics system is closely related to its structure. Before the optimization of urban logistics system, what kind of structured logistics network has higher reliability should be first clarified, and the reliability of logistics system should be optimized on this basis. In addition, unimpeded reliability is a comprehensive index to evaluate the network operation status and reliability. Therefore, in the route planning based on unimpeded reliability, the urban logistics system is optimized to maximize the unimpeded reliability of the urban logistics system. In the efficient urban logistics system, in addition to the reliability and smoothness, but also should consider the economy. Since there is a relatively abstract relationship between reliability and cost, here it proposes the reliability distribution model of urban logistics system based on the generalized cost function. The optimization model of the urban logistics system studied in this chapter is a multi-objective optimization covering reliability, smoothness and economy.

6.1 Logistics System Reliability Simulation The reliability of urban logistics system is closely related to its structure. Before the optimization of urban logistics system, we should first clarify what structure of logistics network has higher reliability. That is, when the structure of the logistics © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. Zhang, Reliability Optimization of Urban Logistics Systems, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-19-0630-5_6

135

136

6 Study of the Reliability Optimization Model …

Fig. 6.1 Network with a different number of connection arcs

network has, the logistics system can maintain its reliability and maximize the impact of the failure of a certain node or arc. As shown in Fig. 6.1, the more arcs are, the greater failure of the probability is. However, when a piece of arc in the network fails, a network with a large number of arcs can meet the demand by connecting other arcs at the point, which is more flexible than the network with the smaller number of arcs. When performing the reliability simulation of the logistics system, the reliability of the system is quantified as the degree that the demand point is met, that is, when the shortage rate of the demand point is low, the system has a high reliability. Suppose: (1) (2)

The supply of each supply point is independent of each other, and is subject to a certain probability distribution; The demand of each demand point is independent of each other, and is subject to a certain probability distribution; Symbol Description: I is a collection of supply points, traversed with i; J is a collection of demand points, traversed with j; bi represents the supply of the supply point i, assuming that the supply capacity of each supply point is stable; d j represents the demand of the demand point j, assuming that each demand is subject to a normal distribution, that is d j ∼ N (μ j , δ 2j ); ξ j represents the shortage of the j demand point; f i j represents the amount of the supply point i to the demand point j, and f i j ∼ N (μi j , δi j );  ei j =

1, if supply point i supplies to demand point j ; 0, else

6.1 Logistics System Reliability Simulation

hj =

137



ei j .

Definition: Arc failure: an arc ei j is interrupted in the logistics network. Supply point failure: a supply point i in the logistics network fails to provide a service, that is, the distribution capacity is 0. Reliability of the arc: R(ei j ) = 1 − EDG , E G is out of stock after the arc ei j fails.  Reliability of the supply point: R(i) = 1 − EDG , E G  is out of stock after the supply point i fails. The reliability of the network is given by the out of stock model: Min Z =



ξj =



dj −



f i j ei j

(6.1)

Considering that the supply capacity of each supply point is not remaining, the passing capacity of the arc is infinite. A random simulation method is used to randomly generate the demand of each point and calculate the out of stock of each demand point and obtain the experimental results, were shown in Tables 6.1, 6.2 and 6.3. Analysis the above three tables, for the logistics network demand number of 5, 6, 7, 8, 8, 10, the reliability of the whole network increases with the number of arcs. When h j = 2, the reliability of the arc decreases as the demand point increases, but when h j > 2, the failure of the arc of the network on the reliability of the whole network is almost negligible, due to the failure of an arc in the network that can be met by connecting other arc of the demand. It shows that as the network scale expands, the network flexibility increases. However, when the connection arc reaches a certain number of then continue to increase the arc on improve the reliability Table 6.1 Network reliability when h j = 2 Number of nodes

5

6

7

8

9

10

Network reliability after one arc fails

0.919

0.903

0.892

0.878

0.863

0.851

Network reliability after one point fails

0.176

0.195

0.203

0.214

0.217

0.221

Notes Make bi = 10, μ j = 10, δ j = 2, iteration 5000 times Data Source Xu Liang [1]

Table 6.2 Network reliability when h j = 3 Number of nodes

5

6

7

8

9

10

Network reliability after one arc fails

1.000

1.000

1.000

0.999

1.000

1.000

Network reliability after one point fails

0.180

0.192

0.202

0.219

0.227

0.238

Notes Make bi = 10, μ j = 10, δ j = 2, iteration 5000 times Data Source Xu Liang [1]

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6 Study of the Reliability Optimization Model …

Table 6.3 Network reliability when h j = 4 Number of nodes

5

6

7

8

9

10

Network reliability after one arc fails

1.000

1.000

1.000

1.000

1.000

1.000

Network reliability after one point fails

0.201

0.213

0.222

0.239

0.247

0.257

Notes Make bi = 10, μ j = 10, δ j = 2, iteration 5000 times Data Source Xu Liang [1]

becomes no longer significant of the whole network, indicating that the network system has reached saturation. With the increase of the connection arc, the ability of the network to resist the point failure has not been significantly improved, so for complex networks, increasing the connection arc does not have much impact on the reliability of the whole system, and the key to improving the system reliability is to improve the point reliability.

6.2 Identification of the Critical Section In the case of certain needs, logistics enterprises can understand the needs of customers in advance, so as to reasonably ensure the inventory level of transportation. However, with the continuous development of urban economy, the needs of urban residents have become more diversified and random, which puts forward higher requirements for the distribution of logistics enterprises. As shown in Fig. 6.2, the figure draws the demand curve with SPSS according to the customer data of a logistics enterprise. The customer demand is fluctuating randomly, and the change of demand may affect the inventory strategy and distribution strategy of the enterprise.

Fig. 6.2 The customer demand curve of a logistics enterprise

6.2 Identification of the Critical Section

139

If the strategy remains under the original deterministic demand conditions, it may cause inventory backlog or out of stock, and the distribution path is unreasonable. Therefore, it is necessary to consider the urban logistics line optimization under the random needs, so as to improve the reliability of the whole system. Road network is one of the most important infrastructure of urban logistics. When an emergency occurs, the road network will be affected to varying degrees, and its service capacity will be reduced, thus having an impact on the urban logistics and economic life. For example, in the event of major natural disasters such as an earthquake or terrorist attacks, some sections of the road may completely lose their function and the road network is paralyzed [2]. In order to effectively improve the network reliability, it is necessary to find out the key road sections that make great contributions to the network reliability. Key sections are defined as the road network vulnerable to damage (including physical damage and congestion damage) and have a greater impact on the road network after the damage. The key sections reflect the overall ability of the road network to resist the disaster impact, is the key point of the overall performance of the road network, and also vulnerable to the disaster impact [3]. The structure of the system determines the function of the system, and the general network system has been basically determined in its planning and design stage. If the transformation in the subsequent implementation stage requires more funds, it may result in a waste of human and financial resources. Research shows that if about 80% of the costs and events occur in the design stage, then 20% of the cost is required in the operation stage of the system; in turn, if only 20% of the money and time are invested in the redesign stage, about 80% of the cost and time is required in the operation stage [4]. Therefore, it is important to design a reasonable structure and stable operation of the traffic network. Connectivity reliability is a reliability index to study the structure of urban logistics system, so it is crucial to consider connectivity reliability in the optimization design stage of the network. The concept of connectivity reliability is based on the reliability of other network systems, but the optimization of urban logistics network considering connectivity reliability is relatively little. This study introduces the concept of road section importance, and find the most important section of the network to improve its reliability. It is of very important significance to maintain the reliability of the road network under normal and emergencies. In the reliability engineering, the importance of the road sections is generally used to describe the reliability of the network. The key sections have an important impact on the reliability of the system. By analyzing the key sections in the network, the reliability of the network can be effectively improved [4]. In a network G = (N , A), N represents the node of the network, A represents the edge of the network, blocking a certain number of network edges (i.e. the edge is lost function and no traffic passes through), the combination of edges that maximize or minimize the overall network traffic loss is calculated through the model, that is, the key edge in the network, the network regions composed of these edges are the networks with the highest or lowest vulnerability [5].

140

6 Study of the Reliability Optimization Model …

max Z =

 o

s.t.



T Fod Z od

Yk . + Z od ≥ 1

Z od ≤ (1 − Yk ) Yk ≥ 1 −



X j , j ∈ k

Yk ≤ (1 − X j )   Xj =  Yk =  Z od =

(6.2)

d

Xj = p

1, When the side j is blocked 0, else

(6.3) (6.4) (6.5) (6.6) (6.7)

(6.8)

1 The path k is still connected when the p edge is blocked 0 else

(6.9)

1 Unconnected path between the starting and ending points 0 else

(6.10)

T Fod represents the traffic between the starting and ending points, k represents a path set, j represents an edge set, Z od represents a set of all paths that constitute connectivity between the starting and ending points, k represents a set of edges that constitute the path k, p represents the number of blocked edges [6]. Determine the X j , Yk , Z od from the different p values. First, map the network structure of the research object, number the nodes and edges of the network, and consider assigning the edges to the traffic between the nodes. Secondly, the paths between the nodes are enumerated to produce a set of paths between each node pair. Finally, the enumerated results are optimized combined with the assignment of various nodes and edges to obtain the set of edges that have the greatest impact on the entire network [6]. The reliability of the critical sections decreases, and then the network reliability will decline a lot. Before carrying out the optimization research of urban logistics system, it is necessary to find out the weak links in the network, by analyzing the weak links in the network, and carry out reliability optimization research for the critical sections.

6.3 Logistics System Optimization Based on Mobility Reliability

141

6.3 Logistics System Optimization Based on Mobility Reliability With the development of science and technology and the accelerating urbanization process, the urban economy develops rapidly. However, many negative problems in urban development have gradually become prominent, such as the urban deterioration and the shortage of resources. Among them, traffic congestion is a relatively serious problem. The quality of urban traffic not only affects the travel of citizens, but also has a great impact on the efficiency of urban logistics. In order to efficiently meet the logistics needs of cities, it is urgently necessary to build a smooth and reliable urban logistics system. In reality, there are many random factors that affect the efficiency of the urban transportation network. On the one hand, the customers’ needs are usually based on the prediction, there are errors in the value, and the customers’ needs usually fluctuate. On the other hand, due to the impact of some emergencies, such as bad weather, natural disasters will lead to the failure of the logistics center [7]. Therefore, customer needs may not necessarily be met. If considered from the perspective of improving prediction accuracy, it have limitations and cannot fundamentally address the impact of demand uncertainty. Reliability is an important measure of the service ability of the network under the influence of random factors. The normal service capacity of the city can meet the needs of production and life. However, due to the impact of emergencies, urban traffic flow will change randomly. If the unblocked reliability of the network is too low, it may cause goods to reach the customer point in time [8, 9]. Based on the theory of unblocked reliability, we study the optimization of urban logistics system distribution, improving the traffic efficiency of the network, and reducing the negative impact on logistics in the case of line failure. Unblocked reliability refers to the probability that the road traffic operation state can meet the unblocked state within the specified time and the road network is under normal use conditions. In the road network system, the road unit capacity reliability can be defined as the probability [10] that a vehicle on a road section or intersection unit can drive at a smooth service level within a certain period (generally peak time). Set unblocked reliability of road unit is Wi , then: Wi =

Number of road section i unblocked during rush hours Total observations of road section i during rush hours

(6.11)

The logistics distribution transportation network is a collection of all possible paths connected between the distribution nodes based on the existing transportation network. The logistics distribution transportation network is composed of multiple distribution paths, while each distribution route is composed of multiple distribution nodes. Distribution node is the most basic component unit in the network, which shows that the determination of the unblocked reliability between the distribution nodes is the basis for the unblocked reliability analysis of the logistics distribution

142

6 Study of the Reliability Optimization Model …

and transportation network. From the perspective of traffic accessibility, the vehicle operation between the two connected distribution nodes can be regarded as a traffic between an OD pair, and there are multiple paths to choose. Therefore, based on the basic theory of unblocked reliability, we can believe that the unblocked reliability between the two distribution nodes is equivalent to an OD pair in the road network, thus using the OD pair unblocked reliability formula for smooth reliability calculation between two distribution nodes [11, 12]. According to the characteristics of logistics distribution, each distribution line is a series system composed of multiple distribution nodes. Using the probability theory, we can conclude that the unblocked reliability of each distribution line is the product of the unblocked reliability between all the distribution nodes on the line. If the unblocked reliability of a distribution line is Wn , then:  Wi j (6.12) Wn = where Wi j is the smooth reliability of node i to node j in this distribution line. In the logistics distribution network system, because the scale and quantity of each distribution line will be different according to the actual arrangement, so the importance of each distribution line is different. Importance is determined based on the proportion of distribution points owned by each distribution line in the number of customers throughout the system. On the basis of the unblocked reliability evaluation of each line, the unblocked reliability of the distribution and transportation network can be obtained through the weighted sum. Assuming that the unblocked reliability of the distribution network is W, there is:  (6.13) W = ξn W n ξn where Wn is the smooth reliability of n distribution line in the distribution network;  is the importance of n line in this distribution transportation network, and ξn = 1, n is the total number of distribution lines in this distribution network.

6.3.1 Problem Description The optimization problem of the random demand logistics distribution system based on unblocked reliability can be described as follows: as shown in Fig. 6.3, the supplier collection s, distribution center collection D and the customer collection C are distributed in the area. Define a full graph G = (V, A, C), V = {0, 1, . . . , n, n + 1} as the vertex set of the graph, where 0 represents the distribution center when leaving, 1, …, n represents the customer point, and n + 1 indicates the return to the distribution center. A = {(i, j) : i, j ∈ V, i = j} is a collection of arcs between two points, with no arc starting at n + 1 points, and no arc terminating at 0 points.

6.3 Logistics System Optimization Based on Mobility Reliability

143

Fig. 6.3 Logistics network structure

d = {di j : i, j ∈ V, i = j} is a set of distance between two points, satisfying the symmetry and triangular inequalities, where d(i, j) ≤ d(i, k) + d(k, j), Ci j represents the cost corresponding to the arc (i, j). With single variety, unified model, the maximum demand for Q, single customer maximum demand is less than Q. Any user demand is an independent random variable ξi , obeying a discrete distribution, with K + 1 probability ξ k (k = 0, 1, . . . , K ), corresponding to probability pi (k) = P(ξi = ξ k ). The goal is to construct a path with the minimum transportation cost, the maximum unblocked reliability, low operation cost and high unblocked reliability under the condition of known user demand probability and considering the road smoothness.

6.3.2 Basic Assumptions and Symbol Descriptions 6.3.2.1

Model Assumptions

The optimization objective of urban logistics system based on unblocked reliability is to ensure the unblocked reliability of road network and realize the low cost of urban logistics distribution. The model targets maximum unblocked reliability and minimum delivery costs, considering the following constraints [13]. Assumption 1: each logistics distribution center can meet the demand of multiple demand points at the same time, but the demand of each demand point can only be met by one logistics distribution center; Assumption 2: there is no limit on the supplier’s supply capacity; Assumption 3: the distribution center has no capacity and flow restrictions; Assumption 4: each distribution point must be visited once during the distribution process; Assumption 5: it distributes a single variety of goods;

144

6 Study of the Reliability Optimization Model …

Assumption 6: the same vehicle model in the distribution center; Assumption 7: the needs of each customer point are independent of each other, and are subject to a certain probability distribution; Assumption 8: one path failure is allowed on each distribution line; Assumption 9: each distribution vehicle starts from the distribution center and serves one line before returning to the distribution center. 6.3.2.2 Cij A1 P O O Un Qn qn K dij Wkln ωk ξi

Symbol Description Vehicle costs (yuan/km); Cost of stock shortage; Fuel price of the vehicle (yuan/L); In traffic congestion, the vehicle fuel consumption of 100 km (L/100 km); In traffic normal state, the vehicle fuel consumption of 100 km (L/100 km); The unblocked reliability of article n distribution line in the system; Capacity of the vehicles serving the n-th line; Demand for the distribution lines in the system; Number of vehicles for the distribution center; Driving distance of the vehicle; Probability of failure in the k-th indicator of the n-th line (k = 1, 2, 3, 4, 5); Impact degree of the article k-th index; Customer point demand;

6.3.3 Model Building According to the Chap. 4 analysis, there are five factors affecting the reliability of urban logistics system, respectively expressed as: k1 , k2 , …, k5 ; k11 , k12 , …, k15 indicate specific indicators of the influencing factor information, and the system reliability is expressed as: R = Rkln1 · Rkln2 · Rkln3 · Rkln4 · Rkln5 = {ωk1 · (1 − Wkln1 ) · [ωk11 · (1 − Wkln11 ) + ωk12 · (1 − Wkln12 ) + · · · + ωk15 · (1 − Wkln15 )]} · {ωk2 · (1 − Wkln2 ) · [ωk21 · (1 − Wkln21 ) + ωk22 · (1 − Wkln22 ) + · · · + ωk26 · (1 − Wkln26 )]} · . . . · {ωk5 · (1 − Wkln5 ) · [ωk51 · (1 − Wkln51 ) + ωk52 · (1 − Wkln52 ) + · · · + ωk56 · (1 − Wkln56 )]}

(6.14)

The cost of the system includes transportation cost, cost of out of stock, and congestion economic loss, and the economic function of the system can be expressed

6.3 Logistics System Optimization Based on Mobility Reliability

145

as: A = xi j · di j · Ci j + K · P(O  − O) + A1 (



ξi −



Q n · xi j )

(6.15)

Considering the smoothness of the system with the urban traffic operation condition as the constraint condition, that is, under the prescribed time and conditions, the unblocked state of urban logistics system operation as the indicator. The reliability of the unblocked urban logistics system is expressed as: U=



μn Un

(6.16)

Establishing a target function of multi-objective optimization model: min Z 1 = 1 − R = 1 − {ωk1 · (1 − Wkln1 ) · [ωk11 · (1 − Wkln11 ) + ωk12 · (1 − Wkln12 ) + · · · + ωk15 · (1 − Wkln15 )]} · {ωk2 · (1 − Wkln2 ) · [ωk21 · (1 − Wkln21 ) + ωk22 · (1 − Wkln22 ) + · · · + ωk26 · (1 − Wkln26 )]} · . . . · {ωk5 · (1 − Wkln5 ) · [ωk51 · (1 − Wkln51 ) + ωk52 · (1 − Wkln52 ) + . . . + ωk56 · (1 − Wkln56 )]} min Z 2 = xi j · di j · Ci j + K · P(O  − O) + A1 ( Max Z 3 = 1 − U = 1 −





ξi −



Q n · xi j )

μn Un

(6.17) (6.18) (6.19)

where Formula (6.17) indicates the minimum unreliability of the system, Formula (6.18) means the minimum operating cost, and Formula (6.19) indicates the minimum smoothness of the system. s.t Wn =

 xi jk =



Wi j

(6.20)

qn ≤ Q n

(6.21)

N≤K

(6.22)

1 The vehicle k is run from customer point i to customer point j 0 else  1 Vehicle k delivery to customer point i yik = 0 else

(6.23)

(6.24)

146

6 Study of the Reliability Optimization Model … n 

x0ik = 1, i = 1, 2, . . . , n; k = 1, 2, . . . , K

(6.25)

x j0k = 1, j = 1, 2, . . . , n; k = 1, 2, . . . , K

(6.26)

xi jk = y jk , j = 1, 2, . . . , n; k = 1, 2, . . . , K

(6.27)

xi jk = yik , i = 1, 2, . . . , n; k = 1, 2, . . . , K

(6.28)

i=1 n  j=1 n  i=0 n  j=0

ξi ≤ Q

(6.29)

where Formulas (6.17), (6.18) and (6.19) are objective functions with the least reliability; the minimum expected length cost (including vehicle driving, economic losses caused by congestion and out of stock cost) is minimum; unblocked reliability is minimum. Formulas (6.20) ~ (6.29) is a constraint; Formula (6.20) means that the unblocked reliability of each distribution line is the product of all distribution nodes on the line; Formula (6.21) means that the amount of goods delivered on the article n line is less than the load weight of the distribution vehicle; Formula (6.22) means that the number of lines requiring the delivery service is less than the number of vehicles in the logistics center; Formula (6.23) means that each customer is in one path; Formula (6.24) means that only one path per vehicle; Formula (6.25) means that the vehicle k must start from the distribution center; Formula (6.26) means that the vehicle k must return to the distribution center; Formulas (6.27) and (6.28) guarantee that each customer has and only one vehicle service; Formula (6.29) means that one vehicle can serve at least one customer. This book intends to solve the above multi-objective planning model into a single target problem, with the weighted coefficient of Z 1 , Z 2 , Z 3 is ϕ1 , ϕ2 , ϕ3 (ϕ1 + ϕ2 + ϕ3 = 1) respectively. To convert a multi-objective into a single objective, penalty factor α1 , α2 are introduced in Formulas (6.17) and (6.19) to indicate the loss when the system is unreliable and unblocked. Thus, to construct a new objective function: min Z = ϕ1 α1 {1 − ωk1 · (1 − Wkln1 ) · [ωk11 · (1 − Wkln11 ) + ωk12 · (1 − Wkln12 ) + · · · + ωk15 · (1 − Wkln15 )]} · {ωk2 · (1 − Wkln2 ) · [ωk21 · (1 − Wkln21 ) + ωk22 · (1 − Wkln22 ) + · · · + ωk26 · (1 − Wkln26 )]} · . . . · {ωk5 · (1 − Wkln5 ) · [ωk51 · (1 − Wkln51 ) + ωk52 · (1 − Wkln52 ) + · · · + ωk56 · (1 − Wkln56 )]}

6.3 Logistics System Optimization Based on Mobility Reliability

147



+ ϕ2 [xi j · di j · Ci j + K · P(O − O)   ξi − Q n · ei j ] + A1 (  μn Un ] + ϕ3 α2 [1 −

(6.30)

6.3.4 Model Solution When required to serve n customers, the entire logistics distribution system requirements may have a different combination of (M + 1)n . If the customer’s demand is an independent random variable, and when the demand is i 1 , i 2 , . . . , i n , respectively, the probability of occurrence is p1 (ξ1 = i 1 ) · p2 (ξ2 = i 2 ) · . . . · pn (ξn = i n ). RV R P (i 1 , i 2 , . . . , i n ) represents the length of the corresponding deterministic optimal path, RV R P represents the optimal desired path length of the vehicle path problem with random requirements, then: E[RV R P S D ] =



p1 (ξ1 = i 1 ) · p2 (ξ2 = i 2 )

i 1 ,i 2 ,...,i n

· . . . · pn (ξn = i n )RV R P (i 1 , i 2 , . . . , i n )

(6.31)

The solution of Formula (6.31) is very difficult with a large number of customers. Therefore, the calculation can be simplified by using remedial stochastic planning methods. The remedial-based stochastic programming method decomposes the VRPSD problem into two phases. In the first stage, the prior sequence is determined based on the stochastic conditions of the customer demand information. According to the probability distribution of customer needs, plan a path back to the distribution center after going through the distribution center. The second stage is adjusted when deterministic information is obtained. The vehicle serves the customer along this path, and takes remedial measures when the path fails [14].

6.3.4.1

Treatment of Random Factors

The customer’s demands can only be known when they arrive at the customer and cannot bypass the customer without demand. When the vehicle travels to a distribution point in the distribution route, and the remaining traffic volume of the vehicle is lower than the demand of the customer point, the distribution vehicle needs to return to the distribution center for replenishment, and return to the failure point to continue the service along the planned route. As shown in Fig. 6.4, according to the planned driving route, the vehicle follows the route of 0 → 1 → 2 → 3 → 4 → 0. When the vehicle reaches the customer Point 3, the remaining volume of the delivery vehicle

148

6 Study of the Reliability Optimization Model …

2

3 The planned driving route

4 1

Actual driving route

5 0 Fig. 6.4 Vehicle remediation strategy

cannot meet the needs of customer Point 3, the vehicle must return to the distribution center 0 for replenishment, and then return to the customer Point 3 for service, so the vehicle route becomes 0 → 1 → 2 → 3 → 0 → 3 → 4 → 5 → 0, leading to an additional driving distance of 3 → 0 → 3 due to task failure.

6.3.4.2

Based on the Remedial Stochastic Planning Model [14] Min Z = Ci j {

n 

n 

[δi s(i, i) + γi s(i, i + 1)]   +K · P(O  − O) + A1 ( ξi − Q n · xi j )} i=0

d(i, i + 1) +

i=1

(6.32)

where  δi =

0, i = 1 [ iQK ]  K −1  K q=1 { K =1 ( r =K +1 Pi (r )) · f (i − 1, q Q − k)}, 2 ≤ i ≤ n  0, i = 1 γi = [ KQ ]  K q=1 { k=1 Pi (k) · f (i − 1, q Q − k)}, 2 ≤ i ≤ n

(6.33)

(6.34)

f (m.r ) = Pr {the total demand for customer 1 . . . m is r }

(6.35)

s(i, j) = d(i, 0) + d(0, j) − d(i, j)

(6.36)

The target function represents the cost of the vehicle from the distribution center to serve all customers to the TSP route of the distribution center, the remedial cost of the vehicle returning to the distribution center for replenishment when a customer point cannot meet the needs of the point, and the economic loss of urban congestion. γi indicates the probability that the vehicle needs at the customer Point i just reaches

6.3 Logistics System Optimization Based on Mobility Reliability

149

the vehicle capacity, δi indicates the probability that the demand at customer Point i exceeds the vehicle capacity when the vehicle arrives, and s(i, j) indicates the distance that the vehicle travels at the Point j after the vehicle capacity reaches Point i and returns to the distribution center to restore capacity.

6.3.4.3

Solution of the Initial Prior Sequence

Given the geographical coordinates and distance between the customer points and distribution centers, MATLAB is programmed to find the initial feasible solution of the corresponding deterministic TSP problem, which serves as the initial solution of the prior sequence.

6.3.4.4

Improvement in the Prior Sequences

The γi , δi function are calculated based on the distribution distance between the customer points, along with an expression for the cost of the expected path, generating the expected cost value of the entire path.

6.4 Reliability Allocation Model for Urban Logistics System 6.4.1 Reliability Allocation Method Reliability allocation is the reasonable allocation of the specified system reliability to various factors. The reliability allocation is related to the importance, complexity, cost of the system constituent units, and the common allocation methods include equal allocation method, consideration of complexity allocation method, proportional combination method, etc. [15].

6.4.1.1

Equal Allocation Method

Equal allocation method is an unconstrained distribution method, regardless of the particularity of the components, but simply averagely assigning reliability to the components. For systems connected in series by n of the same units, the system reliability is R*, and the reliability assigned to each unit is: Ri =

√ n

R ∗ , i = 1, 2, . . . , n

(6.37)

150

6 Study of the Reliability Optimization Model …

The equal allocation method has the advantages of simple computation, but without considering the importance and complexity of each component, it is only suitable for application in the stage of preliminary allocation.

6.4.1.2

The Allocation Method Considering Complexity

The complexity of reliability is considered from the number of subsystems constituting the system, and if a subsystem i contains more units, the more complex the subsystem, its basic formula is: Ni u i = n i=1

Ni

(6.38)

where u i is the complexity coefficient of the subsystem; N i is the number of units contained by the subsystem. The allocation method considering complexity believes that complex subsystems are more prone to failure, so their distribution should be less reliable, but this allocation method has certain limitations due to not considering comprehensive importance.

6.4.1.3

Proportional Combination Method

If a new designed system is very similar to the old one in the types of units forming the system, and the new system appears to meet the new requirements for reliability in the new situation, so it is legal to use the proportional combination method. Based on the failure rate of each unit in the old system, we divide the reliability for each unit in the new system in accordance with the reliability requirements of the new system. The reliability assigned to each unit shall be: Ri N = RsN •

RiO RsO

(6.39)

where Ri N is the reliability of unit i in the new design system; RsN is the reliability requirement of the new design system; Ri O is the reliability of unit i in the old system; RsO is the reliability requirement of the old system [16].

6.4.2 Reliability Allocation Principle In the process of reliability distribution of urban logistics system, the model construction should be carried out while considering the unit technical level, importance,

6.4 Reliability Allocation Model for Urban Logistics System

151

complexity, and other external influence factors, and certain principles should be observed. (1)

(2)

(3)

(4)

In the urban logistics system, units with mature technology can guarantee strong reliability, or it is expected that the actual operation reliability can be improved to a strong level, which can be allocated to a higher reliability. In the urban logistics system, units requiring continuous operation or harsh working conditions and high difficulty to ensure reliability shall be assigned to low reliability. The more sub-factors affecting urban logistics, the more difficult it is to reach the reliability of the logistics system, and increasing its reliability will lead to a sudden increase of cost. Therefore, this book reduces the economy by increasing the improvement factor constraint, so as to ensure the low reliability of the unit allocation. The higher the importance of the factors affecting urban logistics, the greater the impact on the system reliability, even if increasing the reliability will cause the cost improvement, we should ensure that the reliability should reach a certain level. Therefore, improve the economy of the system by increasing the improvement coefficient constraints, so as to ensure the high reliability of the unit distribution with high importance.

6.4.3 Construction of the Model Since there is a relatively abstract concept between reliability and cost, the cost including human resources, material resources, and financial resources to increase the reliability of the unit, it is difficult to obtain the statistics between cost and reliability. In order to overcome this problem, Dale proposes a generalized cost function. The function is built on a model of considering feasible f i , unit minimum reliability Ri,min and unit maximum reliability Ri,max [15]. Ci (Ri , f i , Ri,min , Ri,max ) = e

R −Ri,min i,max −Ri

(1− f i )( R i

)

(6.40)

where the f i values range between 0 and 1, the greater the value indicates the greater the feasibility of improving the unit. The generalized cost function is a nonlinear growth function for cost about units, and achieving maximum reliability theoretically means a considerable cost, but rather a low reliability. This coincides with the actual characteristics of cost growth with reliability. The characteristics of the generalized cost function provide the basis for the integration of the influencing factors in the system reliability distribution. Therefore, this book constructs the reliability distribution model of fresh agricultural products electricity business based on the importance and complexity of the system.

152

6 Study of the Reliability Optimization Model …

min z =

s.t.

n  1 u i ( RRi −Ri,min ) e i,max −Ri ωi i=1 n 

Ri ≥ R ∗

(6.41)

(6.42)

i=1

Ri,min < Ri < Ri,max , i = 1, 2, . . . , n

(6.43)

where Ri is the reliability of unit i; Ri,min is the minimum reliability of the Ri ; Ri,max is the maximum reliability of the Ri ; R* is the reliability of the system to reach; ωi is the unit importance factor; u i is the unit complexity improvement factor; z is the cost target function. The objective function (6.41) of the model is based on the generalized cost function, increasing the importance and complexity improvement factors. First, the importance degree coefficient is introduced by restricting the amplification and reduction of the generalized cost function to the cost of different units. The importance degree coefficient is obtained according to the influencing importance degree of the previous chapter, and the reciprocal of the importance degree ranges between 0 and 1. The higher the importance with smaller reciprocal, the more units of higher importance achieve higher reliability. Secondly, by replacing the feasibility parameter through the unit complexity improvement coefficient, the proportion of the total units of the system represents the complexity of the unit. The higher the complexity of the unit, the lower feasibility of the improvement is, and the cost of the actual input increases. The model constraint Eq. (6.42) indicates that the reliability after the optimal allocation is greater than or equal to the system. Formula (6.43) indicates that the reliability of each unit allocation must be greater than its respective minimum reliability and less than its maximum reliability.

References 1. Liang X. Study on urban road traffic network design problems based on reliability analysis. Beijing: Beijing Jiaotong University; 2006. 2. Jiashi L, Xinsheng Ke. Application of entropy weight factor method in the selection of urban railway train. J Beijing Jiaotong Univ (Social Sciences Edition). 2013;12(3):47–51. 3. Ying T, Chao Y, Xiaohong C. Analysis of road network topological vulnerability and key road sections. J Tongji Univ (Natural Science Edition). 2010;38(3):364–79. 4. Huimin Z, Xuhong Li. Study on the runtime reliability of the highway network. Western China Commun Sci Technol. 2009;8:52–6.

References

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5. Yong G, Huimin W, Jifu G, et al. Evaluation method of road network connectivity reliability based on key road sections. Beijing: People’s Communications Press; 2008. p. 575–9. 6. Matisziw TC, Murray AT. Modeling s-t path availability to support disaster vulnerability assessment of network infrastructure. Comput Oper Res. 2009;36:16–26. 7. Deliang C, Zhiya C. Two-objective opportunity-constrained planning model and algorithm for network reliability optimization. J Central South Univ For Sci Technol. 2011;31(9):160–4. 8. Xiangfeng H. Research on key road section management based on disaster emergency traffic support. Changsha: Southwestern Jiaotong University; 2008. 9. Longzheng L, Yuehong J. Analysis of logistics distribution process under e-commerce. Logistics Technol. 2009;12:36–9. 10. Jianming C, Xiamiao Li, Guanghua Y. Selection of earthquake disaster emergency logistics transportation path based on time change and reliability. J Railway Sci Eng. 2011;8(5):101–6. 11. Jiru J. Current situation and prospect of urban logistics distribution. Traffic Transp. 2009;4:44– 5. 12. Boesch FT, Satyanarayana A, Suffel CL. A survey of some network reliability analysis and synthesis results. Networks. 2009;54(2):99–107. 13. Qian Z, Xia Z, Jun L, et al. Optimization study on urban logistics, distribution and transportation network based on unblocked reliability. Highway Eng. 2011;36(2):38–42. 14. Siming C, Huibin M, Qin W. Economic thoughts on public emergencies in China. J Tongji Univ (Social Sciences Edition). 2008;(5). 15. Shensi X. Reliability distribution model of e-commerce of fresh agricultural products. Beijing Industrial and Business University; 2016. 16. Zhaojun L, Yan Y, Wei S, et al. Calculation of safety measurement system (SMS) reliability index distribution. Nuclear Electron Detection Technol. 2018;05:114–9.

Chapter 7

Case Analysis

7.1 Withdrawal of the Influencing Factors Urban logistics is a complex system, and each link will be affected by internal and external factors in the operation. Effective identification and extraction of various influencing factors are beneficial to the subsequent study of this book. For example, the reliability evaluation index established by selecting appropriate influencing factors can effectively evaluate the reliability of the system and make the research work more scientific and effective.

7.1.1 Build the Matter-Element Matrix Because the urban logistics system is a complex and dynamic system, there are many factors affecting the reliability of the urban logistics system, including political, economic, environmental, transportation and other qualitative and quantitative factors, and each influencing factor is interrelated and affects each other. It is not enough to analyze all influencing factors comprehensively by using only one method, so the combination of matter element analysis and gray correlation calculation is used to evaluate the reliability of urban logistics system. Through the refinement of the evaluation indicators, some qualitative indicators are quantified to quantitatively and reasonably handle various factors affecting the reliability, and accurately judge the indicators affecting the reliability of the urban logistics system. The material analysis to extract the key factors affecting the reliability of urban logistics system are as follows. The first step is to determine the five levels that affect the reliability of urban logistics system: (1) information, (2) operational capacity, (3) technical equipment reliability, (4) policies and regulations, and (5) force majeure. The second step is to determine the classical domain and the section domain based on the rating of the index. Calculate the specific correlation degree of each evaluation index and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 H. Zhang, Reliability Optimization of Urban Logistics Systems, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-19-0630-5_7

155

156

7 Case Analysis

Fig. 7.1 Flow chart of influencing factors of urban logistics reliability

each level, calculate the comprehensive correlation degree of each evaluation index according to the weight of the entropy weight coefficient method, and the final evaluation level is obtained [1]. The evaluation process of the key factors affecting the reliability of the urban logistics system is shown in Fig. 7.1. In the process of reliability evaluation of urban logistics system, according to the characteristics of urban logistics, the attributes of reliability influencing factors are listed one by one, and then various influencing factors are merged and eliminated, appropriate evaluation factors are selected to determine the hierarchy of the evaluation system. Based on a large number of previous studies, this book constructs an evaluation index system that affects the reliability of urban logistics system. The index of urban logistics system reliability is shown in Table 7.1.

7.1.2 Build the Matter-Element Evaluation Model Urban logistics system reliability is a complex and comprehensive problem. By introducing the geophysical analysis algorithm into the reliability evaluation process of the urban logistics system, different interest subjects can be taken comprehensively into account, and quantitative values can be used to express the results of the reliability evaluation, and it can evaluate the reliability of the urban logistics system more comprehensively and objectively. By analyzing the characteristics of certain urban logistics system reliability factors, the range of evaluation indexes of urban logistics system reliability is determined, which is the classical domain in matter element analysis, as shown in Table 7.2. The data were analyzed to obtain the index scores with strong credibility. As shown in Table 7.3.

7.1.2.1

Determine the Matter-Element Matrix to Be Evaluated

Taking the characteristic indicators in the information indicators as an example, the corresponding element matrix to be evaluated is constructed:

7.1 Withdrawal of the Influencing Factors

157

Table 7.1 Reliability index of urban logistics system Primary index

Secondary index

Tertiary index

B1 Information

C11 Storage information

D111 Information workload

C12 Transportation information

D112 Information application rate

C13 Processing and packaging information

D113 Informatization investment

C14 Loading and unloading information

D114 Proportion of IT personnel D115 Information platform utilization

B2 Operation capacity

C21 Node system

D211 Node density D212 Node accessibility

C22 Line system

D221 Road section availability D222 Line efficiency D223 Link density D224 Network connectivity coefficient

B3 Technical equipment reliability

C31 Storage equipment

D311 Investment in technical equipment

C32 Loading equipment

D312 Proportion of advanced technology and equipment

C33 Transportation equipment

D313 Failure rate of technical equipment

C34 Technology

D314 Utilization of technical equipment D315 R & D and innovation investment

B4 Policies and regulations C41 Policy formulation C42 Policy implementation

D411 Necessity of policy formulation D421 Policy recognition D422 Ability of policy implementation subject

C43 Policy performance

D431 Policy implementation effect D432 Policy satisfaction

B5 Force majeure

C51 natural disaster

D511 Extreme weather

C52 Social abnormal events

D521 Infectious diseases D522 Traffic accident D523 Terrorist events D524 Strike

C53 Government behavior

D531 Road restricting

158

7 Case Analysis

Table 7.2 Rating criteria affecting eigenvalues Index Characteristic classification index

Evaluation grade Level 1 basically no impact

Level 2 little Level 3 a impact certain impact

Level 4 great Level 5 impact serious impact

Information

(0.5–1]

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Information (0.5–1] application rate

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Informatization (0.15–0.3] investment

(0.1–0.15]

(0.05–0.1]

(0.03–0.05]

(0–0.03]

Proportion of IT personnel

(0.15–0.3]

(0.1–0.15]

(0.05–0.1]

(0.03–0.05]

(0–0.03]

Information platform utilization

(0.5–1]

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Node density

(0.5–1]

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Node accessibility

1

3

5

7

9

Road section availability

(0.5–1]

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Operation capacity

Technical equipment reliability

Policies and regulations

Information workload

Line efficiency

(0.5–1]

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Link density

(1–1.5]

(0.7–1]

(0.5–0.7]

(0.3–0.5]

(0–0.3]

Network connectivity coefficient

(0.5–1]

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Investment in technical equipment

(0.15–0.3]

(0.1–0.15]

(0.05–0.1]

(0.03–0.05]

(0–0.03]

Proportion of (0.5–1] advanced technology and equipment

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Failure rate of technical equipment

(0–0.02]

(0.02–0.03]

(0.03–0.05]

(0.05–0.07]

(0.07–1]

Utilization of technical equipment

(0.5–1]

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

R & D and innovation investment

(0.02–0.06] (0.015–0.02] (0.01–0.015] (0.005–0.01] (0–0.005]

Necessity of policy formulation

9

7

5

3

1

(continued)

7.1 Withdrawal of the Influencing Factors

159

Table 7.2 (continued) Index Characteristic classification index

Level 1 basically no impact

Level 2 little Level 3 a impact certain impact

Level 4 great Level 5 impact serious impact

(0.5–1]

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Ability of 9 policy implementation subject

7

5

3

1

Policy 9 implementation effect

7

5

3

1

Policy satisfaction

(0.5–1]

(0.3–0.5]

(0.2–0.3]

(0.1–0.2]

(0–0.1]

Extreme weather

1

3

5

7

9

Infectious diseases

1

3

5

7

9

Traffic accident 1

3

5

7

9

Terrorist events 1

3

5

7

9

Strike

1

3

5

7

9

Road restricting

1

3

5

7

9

Policy recognition

Force majeure

Evaluation grade

Data source Yongfe et al. [2]

Table 7.3 Value of characteristic indicators Characteristic index

D111

D112

D113

D114

D115

D211

D212

D221

D222

Index value

0.27

0.34

0.14

0.02

0.05

0.27

7

0.17

0.17

Characteristic index

D223

D224

D311

D312

D313

D314

D315

D411

D421

Index value

0.47

0.27

0.07

0.15

0.017

0.32

0.007

7

0.05

Characteristic index

D422

D431

D432

D511

D521

D522

D523

D524

D531

Index value

7

3

0.15

1

3

3

1

3

7

⎡

N c1 ⎢ c ⎢ 2 ⎢ R = ⎢ c3 ⎢ ⎣ c4 c5

⎤ ⎡ N v1 ⎢ v2 ⎥ ⎥ ⎢ ⎥ ⎢ v3 ⎥ = ⎢ ⎥ ⎢ v4 ⎦ ⎣ v5

D111 D112 D113 D114 D115

⎤ 0.27 0.34 ⎥ ⎥ ⎥ 0.14 ⎥ ⎥ 0.02 ⎦ 0.05

160

7 Case Analysis

Table 7.4 Weight of each index Characteristic index

D111

D112

D113

D114

D115

Index value

0.3

0.3

0.1

0.1

0.2

7.1.2.2

Determine the Classical Domain Matter-Element Matrix and Node Domain Matter-Element Matrix

According to Table 7.2, the evaluation criteria are divided into five levels: (1) basically no impact, (2) little impact, (3) certain impact, (4) large impact and (5) serious impact. The corresponding value range is the classical field R1 ∼ R5 , and the section field R is the value range of the evaluation factors in the table. Calculate the classical domain matter-element matrix R1 ∼ R5 and node domain matter-element matrix Rp [1]. ⎡

⎡ ⎤ ⎤ B1 D111 (0.5 − 1] B2 D111 (0.3 − 0.5] ⎢ ⎢ D112 (0.5 − 1] ⎥ D112 (0.3 − 0.5] ⎥ ⎢ ⎢ ⎥ ⎥ R1 = ⎢ ⎥, R2 = ⎢ ⎥ .. .. .. .. ⎣ ⎣ ⎦ ⎦ . . . . D115 (0.5 − 1] D115 (0.3 − 0.5] ⎡ ⎡ ⎤ ⎤ B3 D111 (0.2 − 0.3] B4 D111 (0.1 − 0.2] ⎢ ⎢ D112 (0.2 − 0.3] ⎥ D112 (0.1 − 0.2] ⎥ ⎢ ⎢ ⎥ ⎥ R3 = ⎢ ⎥, R4 = ⎢ ⎥ .. .. .. .. ⎣ ⎣ ⎦ ⎦ . . . . D115 (0.2 − 0.3] D115 (0.1 − 0.2] ⎡ ⎡ ⎤ ⎤ B D111 (0 − 1] B5 D111 (0 − 0.1] ⎢ D112 (0 − 1] ⎥ ⎢ D112 (0 − 0.1] ⎥ ⎢ ⎢ ⎥ ⎥ R5 = ⎢ ⎥, R = ⎢ .. .. .. .. ⎥ ⎣ ⎣ ⎦ . . . . ⎦ D115 (0 − 0.1]

7.1.2.3

D115 (0 − 1]

Determine the Weight of Each Characteristic Index

Determine the weight of each characteristic index, and the results are shown in Table 7.4.

7.1.2.4

Calculate the Correlation Degree of Each Characteristic Index

The correlation function of each evaluation index is calculated according to the correlation degree formula. As shown in Table 7.5. Calculate the correlation degree of each index belonging to each evaluation set:

7.1 Withdrawal of the Influencing Factors

161

Table 7.5 Correlation function of evaluation index Characteristic index

Correlation function Level 1

Level 2

Level 3

Level 4

Level 5

Information workload

−0.115

0.000

0.300

−0.300

−0.150

Information application rate

−0.200

0.200

−0.500

−0.222

−0.143

Informatizatio investment

−0.500

0.200

−0.200

−0.100

−0.083

Proportion of IT personnel

−0.071

−0.111

−0.250

−0.500

0.330

Information platform utilization

−0.100

−0.167

−0.250

−0.500

0.500

⎡

−0.115 ⎢ −0.200 ⎢ ⎢ K 1 = ⎢ −0.500 ⎢ ⎣ −0.071 −0.100

0.000 0.200 0.200 −0.111 −0.167

0.300 −0.500 −0.200 −0.250 −0.250

−0.300 −0.222 −0.100 −0.500 −0.500

⎤ −0.150 −0.143 ⎥ ⎥ ⎥ −0.083 ⎥ ⎥ 0.330 ⎦ 0.500

Multiply the calculated correlation degree and weight of each index: ⎡

−0.035 ⎢ −0.060 ⎢ ⎢ K 1 = ⎢ −0.050 ⎢ ⎣ −0.007 −0.020

7.1.2.5

0.000 0.060 0.020 −0.011 −0.033

0.090 −0.150 −0.020 −0.025 −0.050

−0.090 −0.067 −0.010 −0.050 −0.100

⎤ −0.045 −0.043 ⎥ ⎥ ⎥ −0.008 ⎥ ⎥ 0.033 ⎦ 0.100

Index Evaluation

The principle of maximum relevance is adopted to classify the influence degree of influencing factors. (1)

(2)

When 0 ≤ K j ≤ 1, it indicates that the influence degree index of influencing factors meets the requirements of a certain level, and its value indicates the degree of meeting the influence level. The larger the value is, the closer it is to the standard of this level. When −1 ≤ K j ≤ 0, it indicates that the influence degree index of influencing factors does not meet the requirements of a certain level.

Comparing the correlation degree between each influencing factor and the influence degree level, among the information factors, the correlation degree of information workload is the highest, indicating that it is the key influencing factor. Similarly, the matter element analysis method can be used to analyze other influencing factors to obtain the correlation function of each evaluation index, as shown in Table 7.6.

162

7 Case Analysis

Table 7.6 correlation function of evaluation index Characteristic index

Correlation function Level 1

Level 2

Level 3

Level 4

Level 5

Node density

−0.115

−0.500

0.300

−0.300

−0.150

Node accessibility

−0.002

−0.003

−0.010

0.005

−0.500

Road section availability

−0.083

−0.188

−0.500

0.300

−0.300

Line efficiency

−0.083

−0.188

−0.500

0.300

−0.300

Link density

−0.054

−0.115

0.000

0.100

−0.150

Network connectivity coefficient

−0.115

−0.500

0.300

−0.300

−0.150

Investment in technical equipment

−0.200

−0.400

0.400

−0.500

−0.333

Proportion of advanced technology and equipment

−0.125

−0.250

−0.500

0.500

−0.500

Failure rate of technical equipment

−0.830

0.830

0.619

0.415

0.208

Utilization of technical equipment

−0.100

0.100

−0.500

−0.143

−0.083

R & D and innovation investment

−0.133

−0.014

0.020

0.400

−0.500

Necessity of policy formulation

−0.500

0.005

−0.003

−0.003

−0.002

Policy recognition

−0.100

−0.167

−0.250

−0.500

0.500

0.005

−0.003

−0.003

−0.002

Ability of policy implementation subject −0.500 Policy implementation effect

−0.002

−0.005

0.010

0.005

−0.005

Policy satisfaction

−0.125

−0.250

−0.500

0.500

−0.500

Extreme weather

0.005

−0.500

−0.003

−0.002

−0.002

Infectious diseases

−0.005

0.005

−0.010

−0.005

−0.002

Traffic accident

−0.005

0.005

−0.010

−0.005

−0.002

Terrorist events

0.005

−0.500

−0.003

−0.002

−0.002

Strike

−0.005

0.005

−0.010

−0.005

−0.002

Road restricting

−0.002

−0.003

−0.010

0.005

−0.500

Calculate the correlation degree of each index belonging to each evaluation set: ⎡

−0.115 ⎢ −0.002 ⎢ ⎢ ⎢ −0.083 K2 = ⎢ ⎢ −0.083 ⎢ ⎣ −0.054 −0.115 ⎡ −0.200 ⎢ −0.125 ⎢ ⎢ K 3 = ⎢ −0.830 ⎢ ⎣ −0.100 −0.133

−0.500 −0.003 −0.188 −0.188 −0.115 −0.500

0.300 −0.010 −0.500 −0.500 0.000 0.300

−0.300 0.005 0.300 0.300 0.100 −0.300

−0.400 −0.250 0.830 0.100 −0.014

0.400 −0.500 0.619 −0.500 0.020

−0.500 0.500 0.415 −0.143 0.400

⎤ −0.150 −0.500 ⎥ ⎥ ⎥ −0.300 ⎥ ⎥ −0.300 ⎥ ⎥ −0.150 ⎦ −0.150 ⎤ −0.333 −0.500 ⎥ ⎥ ⎥ 0.208 ⎥ ⎥ −0.083 ⎦ −0.500

7.1 Withdrawal of the Influencing Factors

⎡

−0.500 ⎢ −0.100 ⎢ ⎢ K 4 = ⎢ −0.500 ⎢ ⎣ −0.002 −0.125 ⎡ 0.005 ⎢ −0.005 ⎢ ⎢ ⎢ −0.005 K5 = ⎢ ⎢ 0.005 ⎢ ⎣ −0.005 −0.002

163

0.005 −0.167 0.005 −0.005 −0.250

−0.003 −0.250 −0.003 0.010 −0.500

−0.003 −0.500 −0.003 0.005 0.500

−0.500 0.005 0.005 −0.500 0.005 −0.003

−0.003 −0.010 −0.010 −0.003 −0.010 −0.010

−0.002 −0.005 −0.005 −0.002 −0.005 0.005

⎤ −0.002 0.500 ⎥ ⎥ ⎥ −0.002 ⎥ ⎥ −0.005 ⎦ −0.500 ⎤ −0.002 −0.002 ⎥ ⎥ ⎥ −0.002 ⎥ ⎥ −0.002 ⎥ ⎥ −0.002 ⎦ −0.500

Determine the weight of each characteristic index, as shown in Table 7.7. Multiply the calculated correlation degree of each index by the weight: ⎤ −0.023 −0.100 0.060 −0.060 −0.030 ⎢ 0.000 −0.001 −0.002 0.001 −0.100 ⎥ ⎥ ⎢ ⎥ ⎢ −0.008 −0.019 −0.050 0.030 −0.030 ⎥ ⎢ K 2 = ⎢ ⎥ ⎢ −0.008 −0.019 −0.050 0.030 −0.030 ⎥ ⎥ ⎢ ⎣ −0.011 −0.023 0.000 0.020 −0.030 ⎦ −0.023 −0.100 0.060 −0.060 −0.030 ⎡ ⎤ −0.100 0.001 −0.001 −0.001 0.000 ⎢ −0.020 −0.033 −0.050 −0.100 0.100 ⎥ ⎢ ⎥ ⎢ ⎥  K 4 = ⎢ −0.100 0.001 −0.001 −0.001 0.000 ⎥ ⎢ ⎥ ⎣ 0.000 −0.001 0.002 0.001 −0.001 ⎦ −0.025 −0.050 −0.100 0.100 −0.100 ⎡ ⎤ 0.001 −0.100 −0.001 0.000 0.000 ⎢ −0.001 0.001 −0.002 −0.001 0.000 ⎥ ⎥ ⎢ ⎥ ⎢ ⎢ −0.001 0.001 −0.001 −0.001 0.000 ⎥  K5 = ⎢ ⎥ ⎢ 0.001 −0.100 −0.001 0.000 0.000 ⎥ ⎥ ⎢ ⎣ −0.001 0.001 −0.002 −0.001 0.000 ⎦ 0.000 0.000 −0.001 0.001 −0.050 ⎡

Table 7.7 Weight of each characteristic index Characteristic index D211 D212 D221 D222 D223 D224 D311 D312 D313 D314 D315 Index value

0.2

0.2

0.1

0.1

0.2

0.2

0.2

0.2

0.3

0.2

0.1

Characteristic index D411 D421 D113 D431 D432 D511 D521 D522 D523 D524 D531 Index value

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.1

0.2

0.2

0.1

164

7 Case Analysis

Comparing the correlation degree between each influencing factor and the influence degree level: in the operation capacity index, node density and network connection coefficient are the key influencing factors; in the technical equipment indexes, the failure rate of technical equipment is the key influencing factor; in the indicators of policies and regulations, policy recognition and policy satisfaction are the key influencing factors; in the indicators of force majeure, extreme weather, infectious diseases, terrorist events and strikes are the key influencing factors.

7.2 Reliability Measurement Based on the most common two-level distribution network in multi-level urban distribution network, this book puts forward the reliability measure model and method of urban distribution network. The reliability of each link of the whole two-level urban distribution network is affected by the five aspects: (1) information, (2) operation capacity, (3) technical equipment, (4) policies and regulations, and (5) force majeure.

7.2.1 Failure Rate Company A mainly has two suppliers supplying goods to its first-layered distribution center D1 , and according to the historical records within one year, their supply ratio to the first-layered distribution center is 5/4. After passing through the first-layered distribution center D1 , the goods are sent to the second-layered distribution centers D21 and D22 according to their destination area, and then the goods are sent from the second-layered distribution center D21 to customers C1 , C2 and C3 according to the customer’s location, from distribution center D22 to C4 and C5 customers. See Table 7.8 for specific ratios (Fig. 7.2). According to the company’s previous historical data, the errors in the distribution process are allocated to the factors affecting the reliability of the distribution network according to the causes, so as to obtain the probability caused by various factors in all errors. As shown in Table 7.9. According to Table 7.9, among the five influencing factors, the probability of distribution failure caused by errors in operation capacity is the largest, followed by Table 7.8 weight of distribution links First-layered distribution center D1

Second-layered distribution center D2

Customer C

W1D1

W2D1

W D21

W D22

WC1

WC2

WC3

WC4

WC5

0.556

0.444

0.636

0.364

0.400

0.300

0.300

0.500

0.500

7.2 Reliability Measurement

165

Fig. 7.2 Distribution network structure diagram

information, while the probability of distribution network failure caused by policies, regulations and force majeure is the smallest. It can be seen that the operation ability of distribution enterprises is very important for each node of distribution network.

7.2.2 Reliability According to the reliability measurement Formula (5.5), we can know the reliability of suppliers: R S1 = W1D1

5 

1 − WkS1 = 0.5557798

k=1

R S2 = W2D1

5 

1 − WkS2 = 0.6357456

k=1

According to the reliability measurement Formula (5.6), we can know the reliability of distribution center and customers: R = 0.8379180 According to the formula for reliability measurement of multi-level urban distribution network Formula (5.7), the distribution network reliability of the company can be obtained through calculation, as follows: R = 0.9984006

0.0044

0.0296

0.0040

0.0004

0.0016

Information (k = 1)

Operation capacity (k = 2)

Technical equipment (k = 3)

Policy and regulations (k = 4)

Force majeure (k = 5)

WkS1

(%)

0.0012

0.0008

0.0048

0.0288

0.0044

WkS2

0.0012

0.0008

0.0024

0.0324

0.0032

WkD1

Table 7.9 Failure rate of each index in each link of urban distribution

0.0008

0.0004

0.0028

0.0328

0.0032

WkD21

0.0008

0.0004

0.0032

0.0320

0.0036

WkD22

0.0012

0.0008

0.0032

0.0308

0.0040

WkC1

0.0012

0.0004

0.0032

0.0316

0.0036

WkC2

0.0012

0.0004

0.0024

0.0320

0.0040

WkC3

0.0008

0.0008

0.0040

0.0300

0.0044

WkC4

0.0012

0.0008

0.0040

0.0300

0.0040

WkC5

166 7 Case Analysis

7.2 Reliability Measurement

167

7.2.3 Degree of Influence According to Formulas (5.8) to (5.11), calculate the influence degree of each index of each link in the whole urban distribution network, as shown in Table 7.10. According to the comparison of influence indicators in Table 7.10, for this distribution network, among the factors affecting the urban distribution network, such as information, operation capacity, technical equipment reliability, policies and regulations and force majeure, the operation capacity has the greatest impact on the reliability of the urban distribution network. In the distribution network composed of suppliers, the first-layered distribution center, the second-layered distribution center and customers, because all goods pass through the first-layered distribution center, each index of the first-layered distribution center has the greatest impact in all links. In addition, the operation capacity of the first-layered distribution center has the greatest influence on the reliability of the city’s distribution network. It can be seen that in order to improve the reliability of the distribution network, we should focus on improving the operation capacity of the first-layered distribution center. In order to meet the needs of the market, multi-layered urban distribution network structure has gradually become the main structural form of urban distribution. In order to ensure the service quality, logistics pays more and more attention to improving the reliability of urban distribution network and ensuring that goods can be delivered to customers in good condition within the specified time. Referring to the measurement method of supply chain reliability, this book puts forward the measurement indicators of urban distribution network reliability: information, operation capacity, technical equipment reliability, policy and regulatory restrictions, force majeure, measures the reliability of dual-layered urban distribution network, and analyzes the influence degree of each index of each link. The failure rate of each index in each link of urban distribution is analyzed according to the statistical data of distribution network failure caused by each factor previously recorded. Therefore, the more and historical data, the more accurate the model will be. For the new multi-level urban distribution network lacking historical data, the reliability can be estimated according to the completeness of each index in each link. By comparing the influence degree of each index, find out the factors that have a great impact on the urban distribution network, and put forward targeted improvement measures, which can effectively improve the overall reliability of the distribution network. Limited by policies and regulations, weather conditions, traffic conditions, urban emergencies, natural disasters, wars and large-scale infectious diseases in force majeure factors are not controlled by legal persons, but urban logistics can formulate risk management plans to strengthen reliability management in three aspects: information, operation capacity and technical equipment, so as to reduce the impact on the reliability of distribution network due to policies and regulations and force majeure. Reduce errors in information entry, processing, storage, etc. There may be errors in the process of information collection, input, transmission, storage and processing, and the programs of computer equipment, and the invasion of the virus will also be

0.000160

0.000022

0.000002

0.000008

3 technical equipment

4 policy and regulations

5 force majeure

0.000024

1 information

2 operation capacity

SkS1

k

s

0.000005

0.000003

0.000021

0.000128

0.000019

SkS2

0.000012

0.000008

0.000024

0.000324

0.000032

SkD1

0.000005

0.000003

0.000017

0.000208

0.000020

SkD211

0.000003

0.000001

0.000011

0.000116

0.000013

SkD22

Table 7.10 Impact degree of each link and index on the whole urban distribution network

0.000005

0.000003

0.000013

0.000123

0.000016

SkC1

0.000004

0.000001

0.000010

0.000095

0.000011

SkC2

0.000004

0.000001

0.000007

0.000096

0.000012

SkC3

0.000004

0.000004

0.000020

0.000150

0.000022

SkC4

0.000006

0.000004

0.000020

0.000150

0.000020

SkC5

168 7 Case Analysis

7.2 Reliability Measurement

169

data errors. Regular computer program maintenance and install anti-virus software to reduce the error rate. In addition, manual operation will also produce errors, such as manual input, employees generally produce more errors than computer mechanical errors, in the input process, such as barcode scanning, so as to reduce manual input errors. Reduce errors during the operations. Each link of urban distribution is inseparable from operation and management, such as unpacking, inspection, packaging, combination, processing, and storage of the goods to the next node. There are many ways to improve operational capacity, such as optimizing vehicle scheduling, inventory control, strengthening operation training, etc. In addition, the application of modern science and technology can also reduce the problems in the operation process, such as the use of barcode technology scanning to reduce the error of manual input, visual management to solve problems in time, and use machinery and equipment to load and avoid manual throwing, falling and unloading caused by cargo damage, etc. In addition, the reliability of the urban distribution network can also be improved by improving the reliability of the technical equipment. The more technical and equipment used in urban logistics, the more conducive to the timely control of the site situation, such as temperature control equipment, RFID, vehicle monitoring system, etc., which not only improves the management efficiency, but also reduces the work loss efficiency. However, technical equipment may also make mistakes. For example, equipment failure, product risks and so on may lead to the delivery service cannot be completed. The probability of technical equipment failure can be reduced by strengthening the maintenance of equipment.

7.3 Reliability Optimization The optimization goal of urban logistics system is to improve the timeliness and reliability of urban distribution. For the complex and changeable urban logistics system, many factors such as natural disasters and emergencies will affect the whole system, and the failure of any link in the system will lead to the collapse of the whole system. Based on the comprehensive consideration of the characteristics of multi variety and small batch of urban logistics distribution, this book puts forward the reliability optimization model of urban logistics system from the two aspects of node reliability and line reliability.

170

7 Case Analysis

Fig. 7.3 Road network structure

7.3.1 Identification of Key Sections Only consider the link of distributing goods from the distribution center to customers, and simplify the structure of a city’s logistics system, as shown in Fig. 7.3. Distribution nodes are abstracted as points, and lines between nodes are abstracted as edges. Number each side in Fig. 7.3 and set up appropriate flow data, as shown in Tables 7.11 and 7.12. Taking p = 5, the results are calculated according to the identification model of key sections. When the combination of 1 (A-B), 12 (F-G), 19 (J-H), 20 (J-K) and 25 (N-O) is blocked at the same time, it has the greatest impact on the overall network reliability. Therefore, 1 (A-B), 12 (F-G), 19 (J-H), 20 (J-K) and 25 (N-O) are the key sections. On the premise of considering the key sections, the reliability optimization of urban logistics system is studied.

7.3.2 Optimization Based on Unimpeded Reliability Only considering the economic and unimpeded objectives in the model in Chap. 6, the urban logistics distribution is optimized. The geographical coordinates of each node are shown in Table 7.13, and A is the distribution center. Calculate the distance between nodes according to the coordinates between nodes, as shown in Table 7.14.

7.3 Reliability Optimization Table 7.11 Number of each side

171 Number

Side

Number

Side

1

A-B

18

E-F

2

A-C

19

E-G

3

A-E

20

F-G

4

A-G

21

F-H

5

A-H

22

G-H

6

A-J

23

G-I

7

A-K

24

H-I

8

A-M

25

H-P

9

A-N

26

I-P

10

B-C

27

J-H

11

B-E

28

J-K

12

B-G

29

J-L

13

C-D

30

K-L

14

C-E

31

L-M

15

D-E

32

M-N

16

D-F

33

N-O

17

D-J

The unimpeded reliability between nodes is shown in Table 7.15, in which the unimpeded reliability between upstream and downstream nodes is different. Under the condition of unblocked reliability, the distribution path of customers is optimized. The demand probability of customers follows the binomial distribution (p = 0.5), and the average out of stock cost A1 is 8.4 yuan/piece. Table 7.16 shows the fuel consumption in road congestion. The number of vehicles in the distribution center is 5, and the vehicle capacity is 1.5 tons. Assuming that the price of diesel is 7.43 yuan/L and the transportation cost of vehicles is 0.73 yuan/km, the congestion fuel consumption takes the middle value, that is, the average fuel consumption per 100 km is 9.8 L under normal conditions and 16.6 L under congestion conditions. The system economy weight φ2 is 0.6 and the system smoothness weight φ3 is 0.4. The penalty factor α2 when the system is not unblocked is 0.4. The importance of line reliability μn is determined according to the proportion of distribution points owned by each distribution line in the number of customers of the whole system. Use MATLAB to program. Number of ants m = 16, maximum number of iterations NC_Max = 200, pheromone importance alpha = 1, heuristic factor importance beta = 5, pheromone evaporation coefficient rho = 0.5, pheromone increase intensity coefficient Q = 100. As an example {B, C, D, E, F, G, J }, the output graph is shown in Fig. 7.4. Similarly, the optimization path is calculated as:A → M → L → K → A;

172

7 Case Analysis

Table 7.12 Flow between nodes A

3125

B

534

2357

C

978

1919

4898

D

564

3524

4724

5125

E

134

1024

976

2043

2046

F

252

525

1010

874

2758

3034

G

56

323

374

187

698

2124

2423

H

37

23

98

99

77

256

1027

1325

I

2198

17

579

394

748

174

574

3754

17

J

287

53

19

23

59

13

57

16

9

1690

K

87

55

25

13

82

9

46

37

78

1143

394

L

105

23

12

51

67

23

17

9

102

467

148

560

M

23

16

9

40

21

30

13

257

256

23

107

225

1923

N

9

9

13

54

18

77

1078

37

2026

16

28

79

574

2323

O

7.3 Reliability Optimization Table 7.13 Node coordinates

173 Number

x

y

A

13.7

7.1

B

21.6

8.2

C

21.4

10.7

D

20.1

12.0

E

23.0

12.0

F

23.0

12.8

G

26.1

11.2

H

24.2

15.3

I

26.9

17.9

J

15.1

13.0

K

7.4

14.6

L

9.5

17.4

M

13.0

19.2

N

18.7

20.0

O

25.6

19.4

Table 7.14 Distance between nodes Distance A

B

A

0.0

8.0 8.5 –

C

D

E

10.5 –

13.1 13.3 –

6.1 9.8 –

12.1 13.8 –

B

8.0

0.0 2.5 –

4.1

–

5.4

–

–

–

–

–

–

–

–

C

8.5

2.5 0.0 1.8 2.1

–

–

–

–

–

–

–

–

–

–

D

–

–

3.0 –

–

–

5.1 –

–

–

–

– –

1.8 0.0 2.9

F

G

H

I

J

K

L

M

N

O

E

10.5 4.1 2.1 2.9 0.0

0.8 3.2

–

–

–

–

–

–

–

F

–

3.0 0.8

0.0 3.5

2.8

–

–

–

–

–

–

–

G

13.1 5.4 –

–

3.2

3.5 0.0

4.5

6.7 –

–

–

–

–

–

H

13.3 –

–

–

–

2.8 4.5

0.0

3.7 9.4 –

–

–

–

4.3

I

–

–

–

–

–

–

6.7

3.7

0.0 –

–

–

–

2.0

J

6.0

–

–

5.1 –

–

–

9.4

–

0.0 7.9 7.1 –

–

–

K

9.8

–

–

–

–

–

–

–

–

7.9 0.0 3.5 –

–

–

L

–

–

–

–

–

–

–

–

–

7.1 3.5 0.0 3.9

–

–

M

12.1 –

–

–

–

–

–

–

–

–

–

4.0 0.0

5.8

–

N

13.8 –

–

–

–

–

–

–

–

–

–

–

5.8

0.0

6.9

O

–

–

–

–

–

–

4.3

2.0 –

–

–

–

6.9

0.0

–

–

–

–

174

7 Case Analysis

Table 7.15 Unblocked reliability between distribution points Side

Unblocked reliability

Side

Upstream

Downstream

A-B

0.71

0.73

A-C

0.65

0.65

A-E

0.62

A-G A-H A-J

Un blocked reliability Upstream

Downstream

E-F

0.72

0.76

E-G

0.63

0.73

0.67

F-G

0.69

0.77

0.71

0.73

F-H

0.74

0.69

0.61

0.65

G-H

0.77

0.75

0.72

0.67

G-I

0.79

0.78

A-K

0.70

0.78

H-I

0.69

0.65

A-M

0.71

0.70

H-O

0.73

0.73

A-N

0.64

0.65

I-O

0.71

0.70

B-C

0.63

0.62

J-H

0.69

0.63

B-G

0.65

0.71

J-K

0.68

0.76

B-E

0.61

0.61

J-L

0.67

0.74

C-D

0.78

0.75

K-L

0.79

0.79

C-E

0.73

0.78

L-M

0.75

0.79

D-E

0.65

0.63

M-N

0.61

0.62

D-F

0.62

0.61

N-O

0.69

0.72

D-J

0.75

0.73

Table 7.16 Fuel consumption in road congestion Traffic flow state

Unblocked

Normal

Crowd

Block

Crowding degree

0 ~ 0.3

0.3 ~ 0.7

0.7 ~ 0.9

0.9 ~ 1

Average road speed (km/h)

> 60

30 ~ 60

10 ~ 30

< 10

Average fuel consumption (L/km)

0.09

0.09 ~ 0.106

0.106 ~ 0.225

> 0.225

Data source Ruijun and Wanxiang [3]

A → N → O → I → H → A; A → J → D → C → E → F → G → B → A; The expected length of the corresponding whole path is: 75.91; 102.98; 93.21 km. The comparison of results before and after optimization is shown in Table 7.17. Through the analysis of Table 7.17, it can be seen that under the condition that the quantity and frequency of customer demand are certain, logistics distribution activities can meet the needs of customers according to the existing distribution strategy and the operation cost can be controlled within a certain range. However, with the ups and downs of economic development and the diversification and personalization of customer needs, the management of urban logistics activities is becoming more and

7.3 Reliability Optimization

175

13

12

11

10

9

8

7 12

14

16

18

20

22

24

26

28

Fig. 7.4 Distribution route

more difficult. If we still carry out logistics distribution activities according to the old distribution strategy, it will affect the timeliness and economy of distribution. In the case of uncertain demand, if the distribution is organized according to the original strategy, the expected length of the path increases by 8.06, resulting in an increase of 2.6% in the whole operation cost. This is only a small part of logistics distribution activities. If the path is not optimized, with the increase of time, the increase of the number of logistics distribution activities will not only increase the cost, but also affect the logistics service level. According to the randomness of customer demand, the path optimization based on remedial stochastic programming method is carried out. Although the new distribution path has no changes in quantity compared with the original path, the results show that the logistics cost is greatly reduced by 12.48% compared with the original strategy. In addition, the cost of the new distribution route under the condition of uncertain demand is 10.15% lower than that of the original route under the condition of determined demand. According to the random demand, the vehicle service route cost obtained by solving the customer’s route based on the remedial random programming method is less, the response speed is faster, and the reliability of the system is higher, which can better meet the random demand changes of urban residents. With the intensification of market competition and the diversification of customer demand, urban logistics can hardly be carried out under the condition of completely known information. If the logistics system cannot be timely and effectively optimized according to the law of the market and the fluctuation of demand, but organizes logistics activities according to the old model and past experience, it is likely to affect the economy and reliability of logistics. This will have an impact on the city’s economy, environment, transportation and people’s livelihood. Hinder the further development of the city and affect the formation of urban core competitiveness. The reliability of urban logistics system has attracted more and more attention from scholars at home and abroad, and the relevant research is still in the exploratory

Objective 240.054 function value

246.03

0.1227

Network unimpeded reliability

0.1227

384.05

Transportation 374.09 cost (yuan)

211.666

0.1484

336.11

272.10

310.91

302.85

After optimization

Expected length of the entire path (km)

Before optimization

Before optimization

A→B→C→D→J→A A→B→C→D→J→A A→M→L→K→A A→E→F→G→A A→E→F→G→A A→N→O→I→H→A A→K→L→M→N→O→I→H→A A→K→L→M→N→O→I→H→A A→J→D→C→E→F→G→B→A

Route

Demand uncertainty

Demand determination

Table 7.17 Comparison of results before and after optimization

176 7 Case Analysis

7.3 Reliability Optimization

177

stage, and further research is needed in the treatment of influencing factors and the selection and improvement of optimization algorithms. Firstly, based on the analysis of the characteristics of urban logistics system, this book defines the factors affecting its reliability. Under the condition of analyzing the action mechanism of various influencing factors, this book uses matter-element analysis to refine the key influencing factors, focusing on the impact of key influencing factors on the reliability of urban logistics. Secondly, the reliability measure model of urban logistics system is established, and the influence degree of five influencing factors on suppliers, distribution centers and customers is discussed. The results show that the operation ability has the greatest influence, especially on distribution centers. Finally, considering economy, smoothness and reliability, a multi-objective optimization model of urban logistics system is established. The economic loss variables caused by congestion in urban logistics distribution are considered in the model, so that the optimization results are more suitable for the characteristics of urban logistics. Through the analysis of an example, the results show that the optimized results are more suitable for the situation of uncertain demand, which proves the effectiveness of the model. The research contents of this book involve several aspects of the reliability of urban logistics system, and many problems need to be solved in future research. A fast and reliable urban logistics system can effectively reduce logistics costs and improve the use value of goods. The optimization of the reliability of urban logistics system is conducive to better adapt to urban demand, improve the efficiency of urban logistics, reduce the congestion caused by distribution vehicles on urban roads, reduce the pollution to urban environment, improve the urban environment and promote the sustainable development of the city.

7.4 Reliability Allocation In order to give consideration to the economy of urban logistics system, based on the generalized cost function, this book puts forward the reliability allocation model of urban logistics system, and takes the e-commerce logistics system of fresh agricultural products in urban logistics system as an example [4].

7.4.1 Analysis on Influencing Factors of Reliability of e-Commerce Logistics System for Fresh Agricultural Products The logistics of e-commerce of fresh agricultural products is a complex system. All links of its logistics will be affected by internal and external factors, resulting in

178

7 Case Analysis

Table 7.18 Failure events in logistics link Link

Events

Transportation

Vehicle failure; low input rate of refrigeration equipment; improper operation of personnel; lax supervision in transit; bad weather; traffic jam; node failure

Storage

Picking equipment failure; refrigeration equipment failure; personnel management error

Loading

Improper operation of personnel

Packaging

Pollution and damage of packaging materials; unreasonable packaging specification and improper operation by personnel

Circulation processing

Incorrect quantity, damage and pollution caused by operator’s misoperation; no precooling or unqualified precooling

Information processing

Error in information system processing; improper operation of personnel; incorrect customer order information

the unreliability of the logistics system. Combined with the characteristics of fresh activity and perishability of fresh agricultural products, we take each link of logistics as the starting point to analyze the fault events affecting the reliability of the whole system, as shown in Table 7.18. In the analysis in Table 7.18, improper personnel operation occurs in many links, so it can be collectively referred to as personnel operation problems, while vehicle faults and picking equipment faults in transportation, storage, packaging and other links can be refined into facility and equipment faults. To sum up, in order to facilitate the follow-up analysis and classify the possible failure events in the logistics link, this book analyzes the reliability of the e-commerce logistics system of fresh agricultural products from four aspects: information technology, facilities and equipment, personnel operation and external environment.

7.4.1.1

Information Technology

The flow of goods is also accompanied by the flow of information. Today, with the rapid development of logistics industry, logistics information as the central nerve of the whole logistics system plays a role in communicating and coordinating all links. The smoothness of urban logistics information directly affects the operation of enterprises. For example, when transporting goods, enterprises must fully understand the demand, traffic volume, weather and other relevant information in order to organize transportation. When loading, unloading and handling, the enterprise shall understand the location and relevant information of the goods, otherwise it may cause repeated handling and affect the quality of the goods. Before storing goods, enterprises should master the current inventory quantity, specification and other relevant information, so as to reasonably allocate the location, improve the utilization rate of the warehouse and provide convenience for delivery.

7.4 Reliability Allocation

179

The impact of information technology on the reliability of fresh e-commerce logistics mainly includes information system errors and order information errors. If there is an error in the logistics information system, it will lead to the difficulty of information transmission, which will affect the reliability of the whole system.

7.4.1.2

Facilities and Equipment

Facilities and equipment are important elements of the logistics system. They shoulder many tasks in all links of logistics and play a very important role in the logistics system. Logistics equipment includes storage, packaging, transportation facilities and equipment, etc. Storage is the guarantee to meet customer needs and deal with emergencies. It is an important part to ensure the reliability of logistics distribution system. The influence of storage equipment on reliability is mainly the failure of picking equipment and refrigeration equipment. Packaging is a basic work, which not only brings convenience to all circulation links, but also protects goods from wind and rain. Packaging is not only an important link to determine the success of distribution, but also one of the important factors affecting the reliability of logistics nodes. The influence of packaging equipment on reliability mainly lies in the weak packaging materials and unreasonable packaging specifications. The influence of transportation equipment on reliability mainly includes the failure of transportation vehicles and the low input rate of refrigeration equipment. The failure of the vehicle during transportation will affect the delivery time of the goods. The low input rate of refrigeration equipment will lead to the temperature of goods in transit and quality problems such as decay and deterioration of goods.

7.4.1.3

Personnel Operation

There is an inseparable relationship between the quality of employees and the reliability of logistics. Personnel operation factors refer to the unreliability of the system caused by the operation and management of specific personnel. Specifically, it includes the problems of managers, primary operators and insufficient training management. The quality of management personnel will affect the reliability of the whole logistics system. The reliability of the logistics system will be affected by the management of storage, the allocation of personnel and vehicles, the management and monitoring in transit and the poor handling of emergencies. The quality of primary operators directly affects the quality of goods. First, picking operation. Picking plays an important role in the whole fresh agricultural product logistics system, which directly affects the accuracy of product quantity and product specification.

180

7 Case Analysis

Second, loading, unloading and handling. Loading, unloading and handling is a logistics activity with high frequency in logistics activities. This link is not only easy to cause damage to goods, but also a link that generates cost and takes time. According to statistics, when the railway transportation distance is less than 500 km, the unloading and handling time will exceed the actual transportation time of goods. The factory produces 1 ton of finished products, which requires loading, unloading and handling up to 252 ton-time. In the process of loading and unloading, if the rough loading and unloading is carried out in violation of the operating procedures, it is easy to damage the cargo packaging and the cargo itself. If the proportion of loading and unloading time is too large, it will affect the receiving and dispatching speed and turnover speed of goods. Therefore, the loading and unloading efficiency and quality of grass-roots personnel will directly affect the quality and cost of products. Third, packaging. Improper packing and incomplete packing will bring inconvenience to storage and delay the distribution of goods. A customer satisfaction survey jointly conducted by SOHU and Legal Daily mainly focused on the service problems of express companies. The difference adjustment showed that 29.76% of customers were “dissatisfied” with the service. Among the reasons for dissatisfaction with express companies, 8.61% chose “losing items due to express companies” and 13.25% chose “poor service attitude of couriers”. The service consciousness and service attitude of primary employees directly affect customer satisfaction and service reliability. Lack of training management means that employees are not trained with sufficient professional quality and service awareness, which leads to the lack of operation specifications, job responsibilities and safety awareness, the lack of safety management awareness and the occurrence of barbaric operation.

7.4.1.4

External Environment

The reliability of e-commerce logistics system for fresh agricultural products is affected by many external factors, including bad weather, traffic congestion and node failure. Bad weather refers to some extreme weather, such as rainstorm, snowstorm, typhoon, etc., which will affect road traffic, hinder cargo transportation, and thus affect the logistics process of fresh agricultural products. For example, in 2012, heavy fog and snowfall occurred in most parts of China. Continuous heavy fog and snowfall in Northeast China, North China, Huang-Huai-Hai Region, the middle and lower reaches of the Yangtze River and South China seriously affected the smoothness of roads and challenged the logistics industry. Many sections of the expressway are closed and Airport Flights are delayed, which affects the speed of logistics. Trucks can’t drive on the road. Some goods can only be stranded in the warehouse and distributed when the weather improves. In addition to the impact on freight volume, transportation costs have also increased.

7.4 Reliability Allocation

181

Traffic accidents such as car crashes and road closures caused by special events will cause traffic congestion, inconvenience to the logistics of fresh agricultural products and affect their reliability. Logistics node, as the place where goods are transferred and distributed, has a very important connection function. It is also the node of information transmission and dispatching management. It is an important part of the whole logistics system. Problems in logistics nodes will have a great impact on the whole logistics system.

7.4.2 Failure Model of e-Commerce Logistics of Fresh Agricultural Commodities Based on Bayesian Network Bayesian network is based on probabilistic reasoning, which can well solve the uncertain and incomplete problems of complex systems and the fault problems caused by correlation. The factors affecting the reliability of e-commerce logistics system of fresh agricultural products are expressed through probability knowledge. It is necessary to build a causal relationship diagram between various information, diagnose and analyze the model to investigate the roles of different influencing factors. Bayesian analysis method can meet the corresponding requirements.

7.4.2.1 1.

Determining Bayesian Network Nodes

Determination of nodes

Determining the nodes of Bayesian network is an important step in building the model. According to the analysis of influencing factors on the reliability of ecommerce logistics system of fresh agricultural products in the previous Sect. 7.4.1, the following 22 network nodes are determined. As shown in Table 7.19. 2.

Defination of nodes

T: System failure. It includes delivery delay, wrong quantity and product specifications and quality problems. One of the three problems is regarded as the failure of the e-commerce logistics system of fresh agricultural products. Delivery delay refers to the failure to accurately deliver the goods to the place specified by the customer within the specified time; wrong quantity and specification refers to that the type or quantity of goods does not comply with the provisions of the customer’s order; quality problems refer to the packaging damage, goods decay and deterioration caused by the failure to maintain the temperature and humidity within the specified range or rough loading and unloading in the logistics process. A1 : The problem of Information technology. It refers to the failure of logistics information technology, including information system failure and order information error.

182 Table 7.19 Description of e-commerce logistics failure parameters of fresh agricultural products

7 Case Analysis Node

Implication

T

System failure

A1

Information technology issues

A2

Facilities and equipment problems

A3

Personnel operation error

A4

External environmental problems

A5

Storage equipment failure

A6

Packaging equipment failure

A7

Transport equipment failure

X1

Information system failure

X2

Incorrect order information

X3

Picking equipment failure

X4

Cold storage equipment failure

X5

Packaging material problem

X6

Packaging specification problem

X7

Vehicle fault

X8

Low input rate of refrigeration equipment

X9

Problems of primary operators

X 10

Management issues

X 11

Inadequate staff training

X 12

Bad weather

X 13

Traffic jam

X 14

Node failure

A2 : The problem of Facilities and equipment problem. It refer to facilities and equipment failures in all links of logistics, including storage equipment failures, packaging equipment failures and transportation facilities and equipment failures. A3 : Personnel operation error. It refers to the operation and management of specific personnel, including management personnel, grass-roots operators and insufficient training management. A4 : The problem of external environment. It refers to Fresh e-commerce logistics being affected by many external factors, including bad weather, traffic congestion and node failure. A5 : The failure of storage equipment. It specifically includes picking equipment failure and refrigeration equipment failure. A6 : The failure of packaging equipment. It includes weak packaging materials and unreasonable packaging specifications. A7 : The failure of transportation equipment. It includes transportation vehicle failure and low input rate of refrigeration equipment. X 1 : The failure of information system. It refers to order processing errors caused by problems in the information system. For example, the picked goods are not displayed

7.4 Reliability Allocation

183

or displayed incorrectly in the system, and the error of the information system results in the wrong quantity and type of goods during outbound picking, which affects the accuracy of outbound goods. X 2 : Order information error. It refers to the delay of subsequent logistics links due to the wrong filling of customer order information. For example, the customer’s receiving address is not filled in in detail and the telephone information is wrong, resulting in the failure to deliver on time in the delivery process. X 3 : The failure of picking equipment. It refers to repeated picking caused by the failure of picking equipment, incorrect picking specifications or damage to commodities, which will cause errors in commodity quantity and specifications and quality problems. X 4 : The failure of cold storage equipment. It refers to the unreasonable control of equipment temperature and humidity in the storage process, which is easy to cause commodity deterioration, lead to cross infection of different types of goods, and finally affect the quality of commodities. X 5 : The problem of packaging materials. It refers to that the packaging materials themselves have quality problems and pollute the goods, or the packaging materials have low compression resistance or poor sealing, which are easy to be damaged in the logistics process, which will lead to the deterioration of the goods. X 6 : The problem of unreasonable packaging specifications. It refer to the failure to take different packaging measures for different goods and improper stowage. For example, eggs are fragile items and should be packed in a more stable way. If the goods are stowed improperly, besides wasting loading and unloading time, it will also cause collision between goods during transportation and cause losses. X 7 : Vehicle failure. It refers to the failure of vehicle parts during transportation, which will affect the delivery time of goods. X 8 : The low input rate of refrigeration equipment. It means that the refrigeration equipment is not reasonably loaded in the process of goods in transit, which will affect the temperature and humidity of goods in transit and cause quality problems such as decay and deterioration. X 9 : The problem of primary operators. The first is picking operation problems, such as wrong labeling and scanning of picking personnel. The second is the barbaric operation of loading, unloading and handling. Throwing, throwing and dragging in the process of loading and unloading are easy to cause problems such as loose packages, holes, deformation and pollution, resulting in damage and loss of goods, resulting in economic losses and quality problems. X 10 : The problem of management personnel. It refers to the inadequate monitoring and management of management dispatchers. For example, the allocation of personnel and vehicles, the management and monitoring in transit and the ineffective handling of emergencies will affect the time and quality of distribution. X 11 : The problem of inadequate training management. It means that employees are not trained to have sufficient knowledge. Therefore, it leads to the lack of employees’ awareness of operation specifications, job responsibilities and safety, the lack of employees’ awareness of safety management and the occurrence of barbaric operation.

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7 Case Analysis

X 12 : Bad weather. It refers to the weather conditions of wind, rain and snow will affect road traffic and hinder cargo transportation, thus affecting the distribution and transportation process. For example, due to the fault of urban roads caused by the earthquake, the goods are damaged or cannot be transported in the city. Due to the influence of snow storm and sand (dust) storm, the road traffic visibility is very low, affecting the timeliness of distribution. X 13 : Traffic congestion. It refers to the congestion caused by traffic accidents and traffic restrictions. Firstly, the delivery time will exceed the regulations. Secondly, due to the extension of in transit time, the temperature of goods may be affected and quality problems may be caused. X 14 : Node failure. It refers to the transportation and conversion difficulties caused by node failures such as logistics distribution centers, airports and highway hubs, which directly affect the delivery time and quality of goods. 3.

Value range of nodes

Determine the value range of each node, and use “0” and “1” to indicate that the event “does not occur” and “occurs”, so the value range of each node is {0,1}.

7.4.2.2

Determining Bayesian Network Structure

At present, the e-commerce of fresh agricultural products in China is still in the development stage, and the data sample statistics on the failure of e-commerce logistics of fresh agricultural products are not perfect, the sample data information is incomplete, and the number of failure accident samples that can be collected is limited. There are abundant literatures on the influencing factors of agricultural products or e-commerce logistics. Therefore, based on the existing research results and expert knowledge, a Bayesian network is constructed, as shown in Fig. 7.5 [4]. As shown in Fig. 7.5, if there is an arc connection between two nodes, it indicates that there is a dependency between the two nodes, such as X 2 and A1 , X 5 and A6 , A3 and T. Conversely, there is no dependency, such as between A1 and A2 , X 1 and X 2 , X 6 and X 7 . The starting point of the arrow is the parent node, and the node pointed by the arrow is the child node. Each node in the Bayesian network has a conditional probability table (CPT), and each child node with a parent node has a conditional probability distribution under the value of the parent node. For example, the CPT of A3 is P(A3 |X 9 , X 10 , X 11 ). The nodes without parent nodes meet the edge distribution. For example, the CPT of X 9 , X 10 , X 11 is P(X 9 ), P(X 10 ) and P(X 11 ).

7.4.2.3

Determine Conditional Probability Table

A complete Bayesian network includes structure and parameters. After determining the network structure, it is necessary to determine the probability relationship between each node, that is, to determine the conditional probability table of each node, which is the basis of Bayesian network reasoning.

7.4 Reliability Allocation

185 X3 X2

X4 X5

A5

X1 A1 X9

A2

T A3

X10

A6

X6

A7 X7

A4

X11

X8 X12

X14 X13

Fig. 7.5 Unreliable Bayesian network of fresh e-commerce logistics system

Due to the limited samples that can be collected, when the accurate probability cannot be obtained, the conditional probability of experts about nodes is obtained by questionnaire, and the triangular fuzzy number method is used for relevant data processing. Triangular fuzzy number establishes a bridge between fuzzy uncertain language variables and determined values. When an evaluation object cannot be measured accurately, it can be transformed into values by using fuzzy evaluation method. After collecting experts’ evaluation on the occurrence probability of events, in order to be related to fuzzy numbers, corresponding language variables are introduced. The corresponding relationship between each language variable and triangular fuzzy numbers is shown in Table 7.20 [5]. The conditional probability evaluation of each node by experts is obtained through questionnaire survey. If the number of experts is q, the language variable of node X i in state

the k-th expert can be converted into triangular fuzzy number j given by k k k k  Pi j = ai j , m i j , bi j (k = 1, 2, . . . , q) according to Table 4.2. Based on the opinions

Table 7.20 semantic value of event occurrence probability and corresponding triangular fuzzy number

Probability range

Triangular fuzzy number

Statement

< 0.01

(0.0, 0.0, 0.1)

Very low

0.01–0.1

(0.0, 0.1, 0.3)

Low

0.1–0.33

(0.1, 0.3, 0.5)

Little low

0.33–0.66

(0.3, 0.5, 0.7)

Medium

0.66–0.9

(0.5, 0.7, 0.9)

Little high

0.9–0.99

(0.7, 0.9, 1.0)

High

> 0.99

(0.9, 1.0, 1.0)

Very high

186

7 Case Analysis

of experts, the evaluation result is obtained. The accurate probability of node X i in a  +2m  +b j state is Pij = i j 4 i j i j . Finally, the conditional probability value of the node: P

P = iPj  is obtained by normalization. ij The conditional probability determined in this book is based on the probability of node occurrence in the e-commerce logistics system of fresh agricultural products that have failed. In actual operation, the failure of logistics system is a small probability event, and not all factors will lead to the failure of the system. The purpose of this book is to find the influencing factors and formation mechanism of e-commerce logistics failure of fresh agricultural products. Therefore, the statistical conditional probabilities are based on the failure accidents that have occurred.

7.4.3 Simulation of e-commerce Logistics Failure Model of Fresh Agricultural Products The purpose of this book is to extract the importance of each influencing factor by establishing the Bayesian network model of e-commerce logistics failure of fresh agricultural products. According to the Bayesian network model established in the previous section, GeNIe software is used for analysis, as shown in Fig. 7.6 [4].

7.4.3.1

Cause Analysis

By comparing the posterior probabilities of nodes, the maximum cause chain of ecommerce logistics system failure of fresh agricultural products is obtained, and the

Fig. 7.6 Bayesian network model simulation of e-commerce logistics failure of fresh agricultural products

7.4 Reliability Allocation

187

Bayesian network diagnostic reasoning model is adopted. Starting from the child node representing the final result, find the parent node with the highest a posterior probability in turn, that is, select the cause with the highest probability from several causes leading to the failure of the e-commerce logistics system of fresh agricultural products, and so on until the last layer of cause is found, The chain composed of these reasons is the biggest cause chain leading to the failure of e-commerce logistics of fresh agricultural products. There may be multiple cause chains leading to the failure of e-commerce logistics system of fresh agricultural products, but this method clearly defines the most possible path, and the probability of other cause chains is relatively small. Using the Bayesian network model established by GeNIe software, the maximum cause chain can be analyzed by calculating the posterior probability of each node under the condition of e-commerce logistics failure of fresh agricultural products. Set the failure node T of the logistics system to the occurrence state, that is, let the state of “T ” be “State1 = 100%”, and obtain the posterior probability of each node through reasoning and analysis by GeNIe, that is, the probability of each node under the condition that the failure of the e-commerce logistics system of fresh agricultural products must occur, as shown in Fig. 7.7. Through the Bayesian network reasoning results, the parent node with the largest posterior probability is found in reverse order along the arrow from the failure node T of the e-commerce logistics system of fresh agricultural products. Among the four parent nodes A1 , A2 , A3 and A4 of T, the posterior probability of A2 is greater than A1 , A3 and A4 to obtain the chain {A2 → T }; Among the three parent nodes A5 , A6 and A7 of A2 , A7 has the largest posterior probability and obtains the chain {A7 → A2 → T }; Among the connected parent nodes X 7 and X 8 of A7 , X 8 has the largest posterior probability. Finally, the causal chain leading to system failure is {X 8 → A7 → A2 → T }, as shown by the chain connected by thick lines in Fig. 7.8.

Fig. 7.7 Cause analysis (1)

188

7 Case Analysis

Fig. 7.8 Cause analysis (2)

According to the maximum cause analysis, facilities and equipment problems have the greatest impact on the failure of e-commerce logistics of fresh agricultural products, and the corresponding probability value is 94%, followed by personnel operation errors, external environment problems and information technology problems. The biggest cause chain of e-commerce logistics system failure of fresh agricultural products is {low input rate of refrigeration equipment → transportation equipment failure → facilities and equipment problems → system failure}. Fresh agricultural products are perishable and vulnerable. In the process of logistics, we must pay attention to the control of temperature and humidity in all links to ensure the quality and safety of products. Therefore, in order to prevent the failure of fresh agricultural products logistics, e-commerce enterprises must pay attention to the investment of facilities and equipment, especially the investment rate of refrigeration equipment, ensure the quality of fresh agricultural products and provide customers with safe and high-quality products.

7.4.3.2

Probability Inference

Probability reasoning using Bayesian network model refers to calculating the posterior probability of other nodes when the state of a node is determined, so as to obtain the impact of the change of the node on other nodes. It is an important method to predict the occurrence of risk and analyze the source of risk. It includes two ways: reasoning the result from the cause and reasoning the cause from the result. The reasoning result from the cause is called risk prediction, and reasoning the cause from the result is called reason inference. Aiming at the failure of e-commerce logistics system of fresh agricultural products, this book makes probabilistic reasoning on information technology, facilities and equipment, personnel operation, external

7.4 Reliability Allocation

189

Fig. 7.9 Bayesian network model of logistics system failure

environment and their detailed factors, and further analyzes the relationship between it and system failure. (1)

Risk prediction

In order to analyze the impact of different nodes’ state changes and node combinations’ state changes on the failure probability of the logistics system, a single evidence variable and evidence variable combination are set by using the Bayesian network model to analyze the impact of different influencing factors on the failure of the e-commerce logistics system of fresh agricultural products. In order to facilitate comparative analysis, firstly, the e-commerce logistics failure model of fresh agricultural products constructed by running GeNIe software is shown in Fig. 7.9. As can be seen from the figure, the probability of occurrence and nonoccurrence of each node can be obtained by the constructed Bayesian network model. ➀

Probabilistic reasoning of single evidence node

In order to study the influence of the state of a single influencing factor on the failure of the whole logistics system, the state change of each node is considered separately, and the posterior probability of the target node is analyzed. As shown in Fig. 7.10, based on the Bayesian network, set “A1 ” as the evidence variable, set the node state of “A1 ” to “State1 = 100%”, and the other node states remain unchanged, indicating that the problem of “A1 ” node must occur, and update the probability of the whole network. At this time, when the probability of “A1 ” occurrence changes from 1 to 100%, the probability of non-occurrence changes from 99 to 0%, and the probability of target node “T ” occurrence changes from 94 to 98%. The same reasoning method is used to analyze “A2 ”, “A3 ” and “A4 ”. When these three nodes change separately, the impact on the output node “T ” is different. As shown in the Table 7.21. When the state of node A2 is changed to “State1 = 100%”, the failure probability of the target node changes the most, followed by A3 , A4 and A1 . Therefore, it shows

190

7 Case Analysis

Fig. 7.10 Impact model of A1 inevitable occurrence on system failure

Table 7.21 Probabilistic reasoning result of single evidence node

Evidence variable state Posterior probability of target node

State1

P(T /A1 )

0.9777

P(T /A2 )

0.9782

P(T /A3 )

0.9677

P(T /A4 )

0.9620

that under the condition that other conditions remain unchanged, the probability of facility and equipment problems has the greatest impact on the probability of failure of the whole system. ➁

Probabilistic reasoning of two evidence nodes

As shown in Fig. 7.11, set “A1 ” and “A2 ” as evidence variables at the same time, and set their node state to “state1 = 100%” in GeNIe, indicating that the two evidence variables occur at the same time, and update the whole Bayesian network model. When the probability of “A1 ” occurrence changes from 1 to 100%, the probability of non-occurrence changes from 99 to 0%, the probability of “A2 ” occurrence changes from 90 to 100%, and the probability of non-occurrence changes from 10 to 0%. The probability of “T ” of the target node has changed from 94 to 100%. Using the same reasoning method, when the other two evidence nodes change, the impact on system failure is shown in Table 7.22. The analysis shows that when the states of two nodes change and the states of other nodes remain unchanged, the impact of two evidence nodes is greater than that of a single evidence node, where the combination of three pairs of node variable state changes directly leads to 100% of system failure, and the other three pairs also lead to the probability of system failure greater than 95%. Three pairs of combinations leading to 100% occurrence of system failure include “A2 ”, which again confirms the

7.4 Reliability Allocation

191

Fig. 7.11 Impact model of A1 A2 inevitable occurrence on system failure

Table 7.22 Probabilistic reasoning result of two evidence nodes

Evidence variable state Posterior probability of target node

State1

P(T /A1 , A2 )

1.0000

P(T /A1 , A3 )

0.9928

P(T /A1 , A4 )

0.9860

P(T /A2 , A3 )

1.0000

P(T /A2 , A4 )

1.0000

P(T /A3 , A4 )

0.9713

result of single evidence node probability reasoning. The probability of “A2 ” node has the greatest impact on the occurrence of system failure. ➂

Probabilistic reasoning of three evidence nodes

As shown in Fig. 7.12, “A1 ”, “A2 ” and “A3 ” are set as evidence variables at the same time, and the node state of the three is set as “State1 = 100%” in GeNIe, indicating that the three evidence variables occur at the same time, updating the whole Bayesian network model. When the probability of “A1 ” occurrence changes from 1 to 100%, the probability of non-occurrence changes from 99 to 0%, the probability of “A2 ” occurrence changes from 90 to 100%, and the probability of non-occurrence changes from 10 to 0%” The probability of “A3  occurrence changes from 71 to 100%, and the probability of non-occurrence changes from 29 to 0%. The probability of “T ” of the target node has changed from 94 to 100%. Using the same reasoning method, when the other three evidence nodes change, the impact on system failure is shown in Table 7.23. The analysis shows that when the states of three nodes change at the same time and the states of other nodes remain unchanged, the impact on the failure probability of the target node is very obvious. The combination of any three pairs of node state

192

7 Case Analysis

Fig. 7.12 Impact model of A1 A2 A3 inevitable occurrence on system failure

Table 7.23 Probabilistic reasoning result of three evidence nodes

Evidence variable state Posterior probability of target node

State1

P(T /A1 , A2 , A3 )

1.0000

P(T /A1 , A2 , A4 )

1.0000

P(T /A2 , A3 , A4 )

1.0000

changes will directly lead to 100% system failure. Therefore, if three influencing factors fail at the same time, the system failure must occur. According to the results of risk prediction, the more the number of node state changes, the more obvious the change to the failure of the whole system. When the state of two nodes changes, the average probability of system failure is 2.1% higher than that of one node. When the state of three nodes changes, the average probability of system failure is 2.94% higher than that of one node and 0.84% higher than that of two nodes. (2)

Reason inference

In the Bayesian network model of e-commerce logistics failure of fresh agricultural products, the reason is deduced from the result and the source of risk is considered. As shown in Fig. 7.13, set the state of “T ” to “State1 = 100%” in GeNIe, indicating that system failure must occur, and update the whole Bayesian network model”. The probability of “A2 ” is increased from 90 to 94%, the probability of “A3 ” is increased from 71 to 73%, and the probability of “A4  is increased from 62 to 64%. The analysis shows that when it is determined that the e-commerce logistics system failure of fresh agricultural products occurs, the probability of “A2 ” is as high as 94%, followed by “A3 ”, “A4 ”, and finally “A1 ”, where the probability of “A1 ” is the lowest, only 1%. This also shows that the probability of system failure due to

7.4 Reliability Allocation

193

Fig. 7.13 Bayesian network model of reason inference

the problems of facilities and equipment is much higher than that of information technology.

7.4.3.3

Sensitivity Analysis

In Bayesian networks, sensitivity analysis refers to the analysis of the impact of different node states on target nodes. The change state of the node needs to be analyzed to further determine the nodes that have a great impact on the variable state of the node. Based on the Bayesian network model of e-commerce logistics failure of fresh agricultural products, this book analyzes the impact of information technology problems, facilities and equipment problems, personnel operation errors and external environmental problems on system failure. As shown in Fig. 7.14, using GeNIe soft-

Fig. 7.14 Sensitivity analysis

194

7 Case Analysis

ware for analysis, when the probability of four influencing factors changes from non-occurrence to occurrence, and other factors remain unchanged, that is, when the probability of state1 of A1 , A2 , A3 and A4 increases evenly from 0 to 1, observe the change of system failure probability. P(T /A1 ) changed from 93.56 to 97.77%, P(T /A2 ) from 53.79 to 97.82%, P(T /A3 ) from 85.87 to 96.77%, P(T /A4 ) from 89.23 to 96.20%. It can be seen that the change of A2 has a great impact on system failure, indicating that A2 is more important than other factors. According to the sensitivity analysis of the influencing factors of e-commerce logistics failure of fresh agricultural products in Fig. 7.14, when the probability values of information technology problems, facilities and equipment problems, personnel operation errors and external environmental problems are changed respectively, their impact on system failure also changes. The steeper the curve is, the greater the difference between the influence values of occurrence and non-occurrence on system failure, indicating that the change range of this factor has a greater influence on system failure. The sensitivity from strong to weak is facilities and equipment problems, personnel operation errors, external environment problems and information technology problems. In order to further analyze the influence of each factor on the system failure, the change of target node caused by the change of node under four factors is analyzed. (1)

Sensitivity analysis of information technology problems

Analyze the influence of information system failure and order information error on the failure of logistics system. As shown in Fig. 7.15, when the probability of two nodes occurring separately increases from small to large, that is, the probability of state1 of X 1 , X 2 increases uniformly from 0 to 1, the probability of system failure increases accordingly. At the initial stage, the impact of the two changes on system failure is similar, and the two curves basically coincide. However, when the occurrence probability of the two is about 0.85, the impact of information system failure on system failure gradually increases, indicating that the impact of information system failure is greater than that of order information error. Therefore, enterprises should 9.90E-01

x1

x2

9.80E-01

system

9.70E-01

failure

9.60E-01

probability

9.50E-01 9.40E-01 9.30E-01

No occur

NODE STATE

Fig. 7.15 Sensitivity analysis of information technology problems

occur

7.4 Reliability Allocation

195

pay more attention to the construction and maintenance of logistics information system. (2)

Sensitivity analysis of facilities and equipment problems

Analyze the impact of storage equipment failure, packaging equipment failure and transportation equipment failure on the failure of logistics system. As shown in Fig. 7.16, when the probability of separate occurrence of three nodes increases from small to large, that is, the probability of occurrence of state1 of A5 , A6 , A7 increases uniformly from 0 to 1, the probability of occurrence of system failure increases accordingly. The steeper the curve, the greater the variation range of this factor and the stronger the sensitivity of this node. The sensitivity from strong to weak is transportation equipment failure, packaging equipment failure and storage equipment failure. The probability of system failure caused by the increasing transportation equipment failure increased from 0.8613 to 0.9782, which was the largest change. (3)

Sensitivity analysis of personnel operation errors

Analyze the impact of three factors on the failure of logistics system: primary operators, managers and insufficient staff training. As shown in Fig. 7.17, GeNIe software 9.90E-01

A5

A6

A7

9.70E-01

system failure probability

9.50E-01 9.30E-01 9.10E-01 8.90E-01 8.70E-01 8.50E-01

NODE STATE

No occur

occur

Fig. 7.16 Sensitivity analysis of facilities and equipment problems 1.00E+00

x9

x10

x11

9.80E-01

system failure

9.60E-01 9.40E-01 9.20E-01

probability 9.00E-01 8.80E-01 8.60E-01

occur

No occur

NODE STATE

Fig. 7.17 Sensitivity analysis of personnel operation errors

196

7 Case Analysis 9.80E-01

x12

9.70E-01

system failure

x13

x14

9.60E-01 9.50E-01 9.40E-01

probability 9.30E-01 9.20E-01 9.10E-01 9.00E-01 No occur

NODE STATE

occur

Fig. 7.18 Sensitivity analysis of external environmental problems

is used for sensitivity analysis. When the probability of three nodes occurring separately increases from small to large, that is, when the probability of state1 of X 9 , X 10 , X 11 increases uniformly from 0 to 1, the probability of system failure increases accordingly. The probability of system failure caused by the increasing transportation equipment failure increased from 0.8828 to 0.9677, which was the largest change. The sensitivity of grass-roots operators is significantly higher than that of managers and staff training. (4)

Sensitivity analysis of external environmental problems

Analyze the impact of bad weather, traffic congestion and node failure on the failure of logistics system. As shown in Fig. 7.18, when the probability of three nodes occurring individually increases from small to large, that is, when the probability of state1 of X 12 , X 13 , X 14 increases uniformly from 0 to 1, the probability of system failure increases accordingly. The sensitivity of node failure is higher than that of traffic congestion and bad weather. Based on the above analysis, among the four influencing factors of information technology problems, facilities and equipment problems, personnel operation errors and external environmental problems, facilities and equipment problems are more sensitive than the other three factors. In the problem of facilities and equipment, the sensitivity of transportation equipment fault is the highest, which also confirms the result of probabilistic reasoning. It shows that e-commerce enterprises of fresh agricultural products should pay more attention to the investment of facilities and equipment to ensure the application of cold chain equipment in transportation and storage, so as to ensure the quality of fresh agricultural products.

7.4.3.4

Impact Effect Analysis

According to the conclusions of cause analysis, probability reasoning and sensitivity analysis, the influence degree of each influencing factor ωi is further determined.

7.4 Reliability Allocation Table 7.24 Node correlation analysis results

197 Node

Mutual info

T

0.34375

A2

0.11111

A7

0.03672

A6

0.02934

A3

0.02632

X8

0.02337

X9

0.01954

A5

0.01557

X5

0.01367

A4

0.01315

X6

0.01298

X7

0.00994

X4

0.00796

X3

0.00674

X 14

0.00441

X 13

0.00178

X 12

0.00097

X 10

0.00055

A1

0.00017

X 11

0.00015

X2

0.00006

X1

0.00003

Let ωi = nMi Mi and M i be the correlation degree of each node relative to its parent i=1 node. The node correlation index is analyzed by GeNIe software, and the analysis results are shown in Table 7.24. The correlation degrees of nodes A1 , A2 , A3 , A4 relative to T are 0.00017, 0.11111, 0.02632 and 0.01315 respectively. According to the calculation formula ω A1 , ω A2 , ω A3 , ω A4 are 0.001128, 0.737048, 0.174594 and 0.087231. The calculated correlation degrees of nodes A5 , A6 , A7 relative to A2 are 0.07182, 0.12676 and 0.15429 respectively. The influence degrees of ω A5 , ω A6 and ω A7 relative to A2 are 0.203531045, 0.359225777 and 0.437243177. The calculated correlation degrees of nodes X 1 , X 2 relative to A1 are 0.00751 and 0.3158 respectively, and the influence degrees of ωx1 and ωx2 relative to A1 are 0.02322848 and 0.97677152. Finally, the influence degree of node X i relative to T is obtained, as shown in Table 7.25.

198 Table 7.25 Impact degree of each node

7 Case Analysis Node name

Impact degree

X1

0.0000262

X2

0.0011015

X3

0.0682939

X4

0.0817182

X5

0.1359025

X6

0.1288642

X7

0.0956707

X8

0.2265986

X9

0.1696118

X 10

0.0039357

X 11

0.0010462

X 12

0.0103367

X 13

0.0190599

X 14

0.0578339

According to the analysis results, the low input rate of refrigeration equipment is the main influencing factor. Followed by the problems of primary level operators, packaging materials, packaging specifications, vehicle faults, cold storage equipment faults, picking equipment faults, node faults, traffic congestion, bad weather, management personnel problems, wrong order information, insufficient staff training and information system faults. The main factors affecting the reliability of e-commerce logistics system of fresh agricultural products are still concentrated in facilities, equipment and personnel operation. Therefore, in order to prevent the failure risk of e-commerce logistics of fresh agricultural products, e-commerce enterprises should improve their service level from the aspects of facilities, equipment and personnel operation, strengthen the investment and improvement of cold chain equipment, improve the quality level of logistics grass-roots employees and improve customer satisfaction.

7.4.4 Parameter Estimation and Test of Reliability Allocation Model Suppose that the relevant data of the logistics system of a fresh agricultural product e-commerce enterprise are shown in Table 7.26. Since the problem or failure of each influencing factor will affect the reliability of the whole logistics system, the influencing factors are connected in series. Since the reliability of the external environment cannot be allocated subjectively, only three influencing factors, information technology, facilities and equipment and personnel operation, are considered. Set the

7.4 Reliability Allocation

199

Table 7.26 Basic parameters of e-commerce logistics system reliability for fresh agricultural products Serial number

Unit name

ωi

Complexity

ui

Ri, min

Ri, max

1

Information technology

0.0012

2

0.1818

0.85

0.999

2

Facility and equipment

0.8075

6

0.5455

0.85

0.999

3

Personnel operation

0.1913

3

0.2727

0.85

0.999

minimum reliability Ri, min of each unit to 0.85 and the maximum reliability Ri, max of each unit to 0.99. Assuming that the reliability of the whole system is required to be R* = 0.83, fmincon of MATLAB is used for optimization, and the results are shown in Table 7.27. The reliability optimization schemes under different distribution methods are obtained through calculation. The equal distribution method is calculated by Eq. (6.37), and the reliability of each factor is 0.9398. The distribution method considering complexity is calculated by Eq. (6.38), and the reliability of each factor is shown in Table 7.27. Finally, the improved cost coefficient method is calculated according to the reliability allocation model constructed in this paper. Although the system reliability obtained by the three methods has reached the required 0.83, the comparison shows that the improved cost coefficient method adopted in this book has the lowest cost under the same reliability requirements, which proves the feasibility of this model. The optimization results show that the reliability of information technology, facilities and equipment and personnel operation is 0.0269, 0.1213 and 0.1245 higher than the initial value of 0.85. Although the importance of facilities and equipment is the highest, its complexity is also the highest. Therefore, under the comprehensive consideration of complexity and importance, the improved reliability of facilities and equipment is slightly lower than that of personnel operation. In addition, the increased reliability allocation value of facilities and equipment and personnel operation is significantly higher than that of information technology. Table 7.27 Reliability of each unit after optimization by different optimization methods Influencing factor Equal allocation method

Allocation method considering complexity

Improved cost coefficient method

Information technology

0.9398

0.9667

0.8769

Facility and equipment

0.9398

0.9034

0.9713

Personnel operation

0.9398

0.9505

0.9745

System reliability 0.8301

0.8300

0.8300

Cost

1571.6502

901.7619

1077.2061

200

7 Case Analysis

Table 7.28 Calculation results of experiment 1

Influencing factor

Rmin

Ri

Information technology

0.8

0.9259

Facility and equipment

0.85

0.9394

Personnel operation

0.9

0.9542

7.4.5 Example Analysis of Reliability Allocation Model 7.4.5.1

Simulation of Different Minimum Reliability

The minimum reliability of each unit in the system is different and other conditions are the same. The influence on reliability allocation is analyzed. The importance coefficient ωi = 1, complexity coefficient u i = 0.6 and maximum reliability Ri, min = 0.99, R* = 0.83 of each link in the system. When the minimum reliability is different, that is, R1, min = 0.8, R2, min = 0.85, R3, min = 0.9. The distribution value of each factor is obtained by solving with MATLAB, as shown in Table 7.28. The reliability of information technology, facilities and equipment and personnel operation increased by 0.1259, 0.0894 and 0.0542 respectively compared with the minimum value, among which the value of information technology reliability allocation increased the most. It shows that when other parameters are the same, the distribution results are different due to the different minimum reliability of each unit. The original reliability of information technology is the lowest, and its reliability allocation increases the most. Therefore, without considering the importance and complexity of the unit, the unit with the lowest reliability has a great impact on the reliability of the whole system.

7.4.5.2

Simulation of Different Maximum Reliability

The maximum reliability of each unit in the system is different, and other conditions are the same. The importance factor of each link in the system ωi = 1,the complexity coefficient u i = 0.6 and the minimum reliability Ri, min = 0.85, R* = 0.83.When the maximum reliability is different, that is, R1, max = 0.95, R2, max = 0.97, R3,ma = 0.99. MATLAB is used to solve the problem, and the distribution value of each factor is shown in Table 7.29. The reliability of information technology, facilities and equipment and personnel operation increased by 0.0096, 0.12 and 0.14 respectively compared with the Table 7.29 Calculation results of experiment 2

Influencing factor

Rmax

Ri

Information technology

0.95

0.8596

Facility and equipment

0.97

0.9700

Personnel operation

0.99

0.9900

7.4 Reliability Allocation

201

minimum value, among which the value of personnel operation reliability allocation increased the most. It shows that when other parameters are the same, the maximum reliability of each unit is different, resulting in different reliability allocation results of each unit. The maximum reliability of personnel operation is the largest, and the increase of reliability allocation is the largest. Therefore, without considering the importance and complexity of the unit, the unit with high maximum reliability has a great impact on the reliability of the whole system.

7.4.5.3

Simulation with Different Importance

The importance of each unit in the system is different, and other conditions are the same. The complexity coefficient of each link in the system u i = 0.6, the minimum reliability Ri, min = 0.85, the maximum reliability Ri, max = 0.99, R* = 0.83, when the importance coefficient is different, that is, ω1 = 0.0012, ω2 = 0.8075, ω3 = 0.1913. MATLAB is used to solve the problem, and the distribution value of each factor is shown in Table 7.30. The reliability of information technology, facilities and equipment and personnel operation increased by 0, 0.14, 0.1296 compared with the minimum value respectively, where the value of reliability allocation of facilities and equipment increased the most. It shows that when other parameters are the same, the reliability allocation results of each unit are different due to the different importance of each unit. Facilities and equipment have the greatest importance and the greatest increase in reliability allocation. Therefore, when the minimum and maximum reliability and complexity of the unit are not considered, it has a great influence on the reliability of the whole system to give priority to the unit with high importance. By using the fitting curve function of MATLAB, the cost added value caused by the increase of reliability of information technology, facilities and equipment and personnel operation is obtained. The horizontal axis represents the change of reliability of each influencing factor, and the vertical axis represents the cost of the change of unit reliability of the system. According to the graph analysis, because of the different importance of different units, the cost of unit reliability change caused by their reliability change is also different. Figure 7.19 shows that when the reliability of information technology factors is [0.86, 0.92], the unit cost of system unit reliability changes increases at a constant speed with the increase of information technology reliability. However, when the reliability of information technology is (0.92, 0.95], the curve slope increases sharply and gradually tends to infinity. This shows that the increase of information technology Table 7.30 Results of experimental 3

Influencing factor

ωi

Ri

Information technology

0.0012

0.8500

Facility and equipment

0.8075

0.9900

Personnel operation

0.1913

0.9796

202 Fig. 7.19 Calculation results of experiment 3

7 Case Analysis 1500

1000

500

0 0.86

0.87

0.88

0.89

0.9

0.91

0.92

0.93

0.94

0.95

reliability will indeed lead to the increase of the reliability of the whole system, but when it increases to a certain extent, the continuous increase will cause a large increase in cost. Figure 7.20 shows that when the reliability of facilities and equipment is [0.86, 0.99], the unit cost of the system unit reliability changes increases at a constant speed with the increase of the reliability of the facilities and equipment. However, when the reliability of information technology is (0.95, 0.99], the curve slope increases sharply and gradually tends to be infinite. Figure 7.21 shows that when the reliability of personnel operation is [0.86, 0.99], with the increase of the reliability of personnel operation, the unit cost of system unit reliability increases at a constant speed. However, when the reliability of personnel operation is (0.95, 0.99], the slope of the curve increases sharply and tends to be infinite. Fig. 7.20 Calculation results of experiment 3

40 35 30 25 20 15 10 5 0 0.86

0.88

0.9

0.92

0.94

0.96

0.98

7.4 Reliability Allocation Fig. 7.21 Calculation results of experiment 3

203 180 160 140 120 100 80 60 40 20 0 0.86

0.88

0.9

0.92

0.94

0.96

0.98

When other parameters are the same, due to the different importance of each unit, the allocation results are different. Facilities and equipment are the most important, and the greater the increase of system reliability caused by the increase of investment is. Therefore, when the maximum and minimum reliability and complexity are not considered, priority is given to the units with high importance, which has a great impact on the reliability improvement of the whole system. The increase of unit reliability will lead to the increase of system reliability and cost. When the reliability increases to a certain extent, the cost will increase greatly if the reliability continues to increase, which will lead to a great increase in the cost of increasing the unit reliability of the system. At this time, the cost change caused by the increase of various factors and the requirements of system reliability should be comprehensively considered, and the reliability of each unit should be allocated. Fresh agricultural products e-commerce logistics system is a complex system, its reliability is affected by many factors, and there is interaction between the factors. In this book, the reliability of fresh agricultural products e-commerce logistics system is studied. Firstly, Bayesian network model is constructed, and simulation analysis is carried out. Through cause analysis and sensitivity analysis, the importance of each influencing factor is obtained. On this basis, the reliability allocation model of fresh agricultural products e-commerce logistics system based on importance, complexity and cost is constructed. In the future research, Bayesian network and other methods can be combined to build the model to get a more accurate network structure, and some variables can be added to make the model closer to the reality. At the same time, the influence of technology maturity and environment on reliability allocation can also be considered in the future.

204

7 Case Analysis

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