Fuzzy Quantitative Management: Principles, Methodologies and Applications (Fuzzy Management Methods) 9811076871, 9789811076879

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Fuzzy Management Methods Series Editors: Andreas Meier · Witold Pedrycz · Edy Portmann

Shaopei Lin Guohua Zhao

Fuzzy Quantitative Management Principles, Methodologies and Applications

Fuzzy Management Methods Series Editors Andreas Meier, Fribourg, Switzerland Witold Pedrycz, Edmonton, Canada Edy Portmann, Bern, Switzerland

With today’s information overload, it has become increasingly difficult to analyze the huge amounts of data and to generate appropriate management decisions. Furthermore, the data are often imprecise and will include both quantitative and qualitative elements. For these reasons it is important to extend traditional decision making processes by adding intuitive reasoning, human subjectivity and imprecision. To deal with uncertainty, vagueness, and imprecision, Lotfi A. Zadeh introduced fuzzy sets and fuzzy logic. In this book series “Fuzzy Management Methods” fuzzy logic is applied to extend portfolio analysis, scoring methods, customer relationship management, performance measurement, web reputation, web analytics and controlling, community marketing and other business domains to improve managerial decisions. Thus, fuzzy logic can be seen as a management method where appropriate concepts, software tools and languages build a powerful instrument for analyzing and controlling the business.

Shaopei Lin · Guohua Zhao

Fuzzy Quantitative Management Principles, Methodologies and Applications

Shaopei Lin Shanghai Jiao Tong University Shanghai, China

Guohua Zhao Shanghai Jiao Tong University Design Research Institute Shanghai, China

ISSN 2196-4130 ISSN 2196-4149 (electronic) Fuzzy Management Methods ISBN 978-981-10-7687-9 ISBN 978-981-10-7688-6 (eBook) https://doi.org/10.1007/978-981-10-7688-6 Jointly published with Shanghai Jiao Tong University Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Shanghai Jiao Tong University Press. © Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 This work is subject to copyright. All rights are reserved by the Publishers, 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 publishers, 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 publishers 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 publishers remain 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

Management guru Herbert Simon once said: “Management is decision-making”. In a complex world, making the right major decisions can be very challenging, especially in today’s VUCA era, where ambiguity, together with volatility, uncertainty and complexity, are all important characteristics of the times. How to make management decisions more scientific, rational, and evidence-based in the era of ambiguity is something that everyone and every organization will pay attention to in today’s era. Especially in major projects, project management and other fields that are highly related to decision-making, further improving the qualitative analysis in the original fuzzy state to quantitative analysis and decision-making based on evidence is a necessary part to think and learn for decision makers. As a master expert in the field of project management in China, Prof. Lin Shaopei has been committed to the development of engineering management and project management education in China for many years. He promotes the International integration and localized innovation of engineering project management, helping Chinese enterprises develop their practice in overseas engineering projects. With his profound academic foundation, personal experience and observation of fuzzy quantitative management in engineering practice, he has written this book meticulously. Based on the theory and methods of fuzzy mathematics, this book combines interdisciplinary theories and methods such as project management, artificial intelligence, system dynamics, etc., and applies fuzzy theory to areas of project investment decision-making, risk analysis, cost forecasting, expert systems, etc. It is demonstrated through specific engineering practice cases to help readers have a profound understanding and mastery of the theory and practice of fuzzy quantitative management. Regardless of ideological, systematic or practical, this book has a very rare reference value for the study of fuzzy quantitative management.

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This book gives us “clear” thinking and cognition in the “fuzzy” era. Thanks to Profs. Lin Shaopei and Dr. Zhao for bringing us this great book! August 2022

Fu Yongkang Founder, Changeway Project Management Shanghai, China

Preface

For management is the highest intelligence of human being, which is represented by the capacity of instantaneously massive fuzzy information processing. Under ever increasing complexity of project environment, it is not affordable for limited human capacity to manage and control it. “Quantitative Management” explores the only path of future development in intelligentization, digitization and network framework; through computer AI technology to simulate the decision intelligence of human being, through network communication to maneuvering information of different aspects and through digitization to define project risks from qualitative to quantitative; it will essentially promoting the development of managerial science. Using computer to simulate human intelligence integrated with fuzzy approach forms the “Fuzzy-AI modeling”, which provides an efficient tool to simulate human intelligence for performing digitized decision inference or quantitative information for management decision. With volatility, uncertainty, complexity and ambiguity (VUCA) era and information overload, it has become increasingly difficult to analyze the huge amounts of imprecise data and information, both qualitative and quantitative, to generate appropriate and reliable argument for decision making. For these reasons it is important to extend traditional decision making process by adding intuitive reasoning, imprecision and human subjectivity. In order to discuss the initiation, development, theoretical framework forming as well as philosophical analysis of “Quantitative Management”, it is related to the paradox of managerial decision under uncertain environment. Since the uncertain of managerial events are really existed, yet uncertain in its degree of performance. One uses probabilistic model to simulate it, is violate to the physical nature of the problem; for management uncertainty is a degree uncertain problem, rather than uncertain in its occurrence. It is appropriate to use fuzzy sets or grey theory as its basis of mathematical modeling. As a matter of fact, in the uncertain objective world, there are two kinds of uncertainties, i.e.: uncertainty in the occurrence of the events; and the degree uncertainty of the events. Even though, many people would use probabilistic model in the past, however it is only appropriate to apply in those events, which is uncertain in its occurrence; nevertheless, in the majority of managerial events, which is exactly occurred, vii

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yet uncertain in its degree of performance. Therefore, the probabilistic model cannot suitable to such situation. To deal with uncertainty, vagueness, and impreciseness, Lotfi A. Zadeh introduced fuzzy sets and fuzzy logic1 ; in commercial business domain fuzzy logic can be applied to extend portfolio analysis, customer relationship management, performance measurement, web analytics and controlling, community marketing and other business domains to improve managerial decisions. Thus, fuzzy logic can be regarded as a management method where appropriate concepts, software tools and languages build a powerful instrument for analyzing and controlling the business. Scientists and practitioners are facing a huge amount of real world problems which cannot be represented by rigorous analytic mathematical model and has concluded as “non-structured problems”, it involve the majority of uncertain managerial problems. The introducing of “subjective information” explored another scenario of solving these real world “non-structured” problems.2 As “subjective information” is the most valuable resource of human being in solving problems and it is the very scientific foundation of using computer to simulate the intelligence of mankind; along this end, one may widely use AI technology in engineering applications, in uncertain economic decision, in risk evaluation etc. In fuzzy solution of practical events, the concept of “subjective information” is directly related to the section of arbitrary fuzzy membership function, the accuracy of membership function remain a key problem of its applicability in practice. Combining sampling analysis and AI machine learning, one can using sampling analysis to modify the membership function until it approaching to satisfactory solution.3 This exploration verifies the convergence of the fuzzy solution to reality from theoretical aspect and thus proves the fuzzy methodology is appropriate to apply in a family of “non-structured” problems including quantitative management. The monograph entitled in “Fuzzy Quantitative Management” is a book which combines fuzzy set deducing and AI technology based on subjective information for illustrating and devoting to fuzzy quantized solution in different sorts of management problems with reasonable philosophical background in various fields of application. By using fuzzy inference philosophy, this book employs the relation of human intelligence with the fuzzy information processing, exploring the path of using AI and knowledge engineering for solving management problems. A series of practical examples are presented in the monograph for reference to the readers, which is especially important in project risk management for those severe and complicated mega projects. Moreover, the monograph has exploring and promoting the quantitative management by fuzzy mathematical model with AI representing as a new discipline in managerial science.

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Zadeh L.A.; Fuzzy Sets. (1965). Information and Control, Vol. 8, 1965. Lingxi Q. (1985). On Subjective Information in Structural Optimization. Journal of Computational Structural Mechanics and Applications, 2(2), 1985 (in Chinese). 3 Shaopei L. (2005). On paradox of Fuzzy Modeling: Supervised Learning for Rectifying Fuzzy Membership Function. Artificial Intelligence Review, 23, 395–405. 2

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This monograph is just a test of exploring the power of fuzzy inference and the art of artificial intelligence for simulating human intelligence in decision making as well as in the associated management events. I hope it will raise broad interests by the researchers and practitioners, and bring to further investigations and studies in this newly explored management discipline. The monograph is based on the synthesis of managerial science with fuzzy sets, AI and knowledge engineering. Through the combination of fuzzy inference and AI technology as a unified model, it can be widely used in solving a family of non-structures managerial problems, modifying the decision maker’s fuzzy inference processes during decision making by computer; including to digging out those implicit information in insight law of objective events as well as in people’s mind for the completeness of the source information and source data for guaranteeing the correctness of Big Data prediction. It further shows the sophistication of “Fuzzy-AI Model”, which can be extended to use in the managerial science and play a promotion in the era of digital revolution. The authors would express their special attitudes to Prof. Andreas Meier4 for his valuable suggestions, which enables the monograph more clarified to approach its goals. Moreover, Ms. Li Ying (Suri) spent a lot of efforts in detail editing and reviewing the manuscripts of the monograph, her contributions to the original manuscript can never be ignored, for which the authors will express to her with their sincere appreciations. Shanghai, China July 2022

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Shaopei Lin Guohua Zhao

Commentary letter from Professor Andreas Meier to Professor Shaopei Lin on “Book Proposal from Springer Series in Fuzzy Management Methods” (2017).

Contents

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Philosophical Consideration of Quantitative Management . . . . . . . . 1.1 Background Information of Quantitative Management . . . . . . . . 1.2 Philosophical Background of Managerial Science . . . . . . . . . . . . 1.3 Philosophical Considerations of “System-Fuzzy” Model . . . . . . 1.4 Philosophical Considerations of “Quantitative Management” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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“Fuzz-AI Model” for Quantitative Management . . . . . . . . . . . . . . . . . 2.1 The Features of Quantitative Management . . . . . . . . . . . . . . . . . . 2.2 What So-Called “Fuzzy-AI Model”? . . . . . . . . . . . . . . . . . . . . . . . 2.3 Theoretical Foundation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 The Meaningfulness of Fuzzy-AI Model . . . . . . . . . . . . 2.3.2 Setting of “Fuzzy-AI Model” . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Fuzzy Distance with Variation Weights . . . . . . . . . . . . . 2.3.4 Fuzzy Distance by Nearness Degree . . . . . . . . . . . . . . . . 2.4 Decision of Long-Term Highway Maintenance Investment . . . . 2.4.1 The Introducing of Pavement Damage State Indicator, PDSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Fuzzy Model for the Decision of Highway Maintenance Investment . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Elliptic Damage Model for Highway Maintenance Investment . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.4 Fuzzy Investment Decision Maintenance Investment of Highway System . . . . . . . . . . . . . . . . . . . . 2.5 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Conceptual Investigation of “Deep Data” and “Deep Knowledge” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 “Deep Data/Knowledge” in a Decision System . . . . . . . . . . . . . . 3.2 Representation of Hierarchical Space of State . . . . . . . . . . . . . . . 3.3 Fuzzy Deep Knowledge Reasoning Approach . . . . . . . . . . . . . . . 3.3.1 Mathematical Distance in Space . . . . . . . . . . . . . . . . . . . 3.3.2 Fuzzy Hierarchy Reasoning Approach . . . . . . . . . . . . . . 3.3.3 Weight Distribution Between Attributes . . . . . . . . . . . . . 3.3.4 Illustrative Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.5 Fuzzy Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Fuzzy Decision in Bidding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Problem Illustration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Mathematical Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Solving Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Fuzzy Quantitative Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Background in Fuzzy Quantitative Risk Management . . . . . . . . . 4.2 Project Risk in the Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Risk Knowledge Framework (RKF) . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Hierarchical Structure of RKF . . . . . . . . . . . . . . . . . . . . . 4.3.2 Functions of RKF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Software Architecture of Knowledge Based Fuzzy Decision Supporting System (KB-FDSS) . . . . . . . . . . . . . . . . . . . 4.4.1 Expression of Risk Through Fuzzy Set . . . . . . . . . . . . . . 4.4.2 Fuzzy Assessment and Quantification of Risks . . . . . . . 4.4.3 System Function of KB-FDSS . . . . . . . . . . . . . . . . . . . . . 4.5 Example Verification of Risk Evaluation . . . . . . . . . . . . . . . . . . . . 4.6 Establishment of the Knowledge Base for Fuzzy Decision-Making of Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Quantitative Risk Decision of Overseas Projects . . . . . . . . . . . . . . . . . . 5.1 Project Risks in Overseas Engineering Market . . . . . . . . . . . . . . . 5.2 Modeling of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 The Contents and Characteristics of Project Investment Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 The Indicator System of Economic Risks in Project Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Soft Strength and Human Error Risk in Overseas Projects . . . . . 5.3.1 Risk Control and “Soft Strength” of Enterprises . . . . . . 5.3.2 Human Factor Induced Decision Error in Overseas Project Risk Management . . . . . . . . . . . . . . 5.3.3 Factors and Traps of Wrong Decision . . . . . . . . . . . . . . . 5.3.4 Psychological Analysis of Decision Trap . . . . . . . . . . . . 5.4 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Real Estate Project Investment Risks in New York City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 High-Speed Railway Investment Risks . . . . . . . . . . . . . . 5.5 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

System Dynamics Modeling and Applied to International PPP Project Risk Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Development Characteristics of System Dynamics . . . . . . . . . . . 6.2 System Analysis of Overseas PPP Project Risks . . . . . . . . . . . . . 6.2.1 The Concept and Characteristics of System Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 The Capacity Expected by System Methodology . . . . . 6.2.3 Elements of System Component . . . . . . . . . . . . . . . . . . . 6.2.4 The System Risks of Overseas PPP Projects . . . . . . . . . 6.2.5 Case Study of System Dynamics Modeling of Project Financing Risk . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.6 The Implementation of Risk Management Overseas of PPP Project . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.7 System Analysis and Action of Risk for Overseas PPP Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Risk Control and System Dynamics Model of Overseas PPP Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 The Advantages of System Dynamics Model . . . . . . . . 6.3.2 The Establishment of System Dynamics Model . . . . . . 6.3.3 Soft Risk Analysis of Overseas PPP Project . . . . . . . . . 6.4 Solution of Soft System Dynamics Model for Overseas PPP Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Six Kinds of Soft Risks in Overseas PPP Project . . . . . 6.4.2 Solution of FAHP Method . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Building Risk Factor Framework in FAHP System Dynamics Model Solution . . . . . . . . . . . . . . . . . 6.4.4 Weight Order of Sub-Risk (Attributes) Determined by Eigen-Value Solution . . . . . . . . . . . . . . . 6.4.5 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.6 Practical Treatment of Overseas PPP Project . . . . . . . . . 6.5 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Fuzzy TOPSIS Method for the Cost Prediction in Bridge Engineering Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 On Fuzzy TOPSIS Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Bridge Cost Prediction Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Investigational Review in the Prediction of Engineering Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Sample Matching Ideas . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Fuzzy Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Method Selection of Cost Prediction . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Indicators of Engineering Cost Prediction . . . . . . . . . . . 7.4.2 Fuzzy Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.3 Fuzzy TOPSIS Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 Data Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Calculation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Advantages of Quantitative Management in Decision . . . . . . . . . 8.1 Two Kinds of Events with Different Nature and Its Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Theoretical Basis and Application Areas of “Fuzzy-AI Model” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Some Modeling Expressions of Quantitative Management . . . . 8.3.1 MP (Mathematical Programming) Model . . . . . . . . . . . . 8.3.2 NM (Nearness and Matching) Model . . . . . . . . . . . . . . . 8.3.3 Max/Min Indicator Model . . . . . . . . . . . . . . . . . . . . . . . . 8.3.4 AE (Assessment and Evaluation) Model . . . . . . . . . . . . 8.4 Quantitative Management Perspectives . . . . . . . . . . . . . . . . . . . . . 8.5 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . On Fuzzy Method of “Deep Knowledge” and “Deep Data” in Project Quantitative Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 The Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Representative Expression of Hierarchical Space of State . . . . . 9.2.1 Multi-Layered Hierarchy Space . . . . . . . . . . . . . . . . . . . . 9.2.2 Mathematical Distance in Space . . . . . . . . . . . . . . . . . . . 9.3 Fuzzy Hierarchy Reasoning Approach . . . . . . . . . . . . . . . . . . . . . 9.3.1 Space Chart Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 Fuzzy State Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.3 Weight Distribution Between Attributes . . . . . . . . . . . . .

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Illustrative Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.1 Space Chart of Event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.2 Fuzzy Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Fuzzy Decision in Bidding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5.1 Mathematical Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5.2 Solving Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Perspectives in Combining Fuzzy and AI Techniques in Quantitative Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.1 Facing Digitization of Project Management . . . . . . . . . . 10.1.2 Case Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.3 What Should We Understand? . . . . . . . . . . . . . . . . . . . . . 10.2 Two Types of Economy and Its Characteristics . . . . . . . . . . . . . . 10.3 Business Mode Under “Internet” Era and Knowledge Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Sustainable Development of Successful Enterprise Under Knowledge Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Case Study—E-commerce and Logistics . . . . . . . . . . . . . . . . . . . . 10.6 Program of Studies on Project Management Under Digital Era and Knowledge Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7 Case Studies—Expert System for Airplane Structural Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7.1 On AI and Expert System for Structural Design . . . . . . 10.7.2 Production System and Inference Network . . . . . . . . . . 10.7.3 The Building of Expert System for Airplane Structural Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.8 Internet + AI Based Engineering Application Systems . . . . . . . 10.8.1 Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . 10.8.2 The AI Exploration for Application Systems . . . . . . . . . 10.9 PMO Under Internet Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.11 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

133 133 133 133 135 135 137 139 145 149 151 151 152 154 158 158 159 159 162 164 165

About the Authors

Prof. Shaopei Lin Fellow of ICE, Royal Charter Engineer, U.K. He has been the board director of PMI Global Accreditation Center, U.S.A. and Chairman of PMI-GAC China regional Center. Executive Director, ICE (Institution of Civil Engineers, U.K.) China Education and Training Center. He serves as a professor in Shanghai Jiao Tong University, China since 1986, devoted to the teaching and research in engineering and management; he devotes himself as the consulting work for more than 50 years. In 1990s, he has been research professor in Cornell University, USA and Hog Kong University. For his contribution in project management education and practice in China, he obtained Life-Long Achievement Award issued by PMI China in 2012. Professor Lin has been authored ten books including four published by Springer Press and near about two hundreds of papers, edited around dozen of Proceedings and special volumes in Engineering and Management the world over.

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Dr. Guohua Zhao Vice president of Shanghai Jiao Tong University Design and Research Institute (SJTUDRI) & President of Urban Development Comprehensive Design and Research Institute (UDCDRI). He obtained Doctor of Business Administration from Otago University, New Zealand and is currently a Senior Engineer, Researcher of Shanghai Jiao Tong University Public-Private Partnership Research Center, PPP Expert of National Development and Reform Commission, Expert of Science and Technology Commission of Shanghai Municipal Housing and Urban-Rural Construction Administration Commission. Mainly engaged in the research of urban planning and economic development, especially in project management and PPP projects consultation works. He is keen in involving managing financial activities as the expert of quantitative management with uncertain approaches. He has 20 more years of project management and consulting experience and has been authored near 10 publications in this field.

Chapter 1

Philosophical Consideration of Quantitative Management

1.1 Background Information of Quantitative Management In the management discipline, there are two different managerial modes, namely: qualitative (empirical) and quantitative (digital) managements. It is ever increasing recognition that under volatility, uncertainty, complexity and ambiguity (VUCA) era, the empirical mode of management, which is based on the heuristic knowledge of single manager (decision maker), is far from enough to cover such a sophisticated environmental changes, under such situation, the digital mode of management through computer will doubtlessly reveal in the stage of management and play its colorful and important role in the development of managerial science. This chapter will illustrate the philosophical considerations of the development in managerial science, and draws out the compatibility of managerial events with its related links, describes how it can be modeled by an AI model with the fuzzy sets’ inference. This is rather practical consideration of abandoning the “exactness” and “pure optimization” solutions; replaced by realistic “acceptable”, “satisfactory” or “reasonable” solutions. Based on above-mentioned statements, the “Fuzzy-AI” model is emerged, based on which, a broad ranges of real world problem can be successfully solved. The evidence of which can be shown through a series of references of practical examples solved by the “Fuzzy-AI” model. Taking account of modern digital era, the emergence of Internet has brought a new scenario of ecology in project management (PM), its methodology, platform, procedures and tools are subjected to subversive changes. So the monograph of “Quantitative Management” is urgently needed for renewing concepts in our PM community. In the knowledge economy nowadays, the concept of how PM is implemented by data processing on the virtual platform through Internet has widely used in PM community; which is different from former industrial economy, while PM is operated with solid assets as its resources and run the project on a real physical manner. This is the impetus and the reason of why “Quantitative Management” is initiated. From strategic point of view, talent cultivation is extremely critical to © Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 S. Lin and G. Zhao, Fuzzy Quantitative Management, Fuzzy Management Methods, https://doi.org/10.1007/978-981-10-7688-6_1

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managerial science development, we need to cultivate our talents accommodating to the digital era and on the “Internet+” platform. Moreover, we are entering Internet+ VUCA era, the characteristics of project management in this era is not only on the Digital Real Time Processing platform, but also it has no Exact Value Processing, all the processed quantities are not the real value but the “acceptable”, “satisfactory” or “reasonable” value at the instantaneous moment, using fuzzy sets inference for quantitative management can be innately suitable to the situation of “Management under VUCA Era”, that is also why the monograph “Fuzzy Quantitative Management” can be accommodating to the development of our era.

1.2 Philosophical Background of Managerial Science In VUCA era, making decision for management issues is extremely difficult as to analyze the huge amounts of data and information for generating appropriate management decisions. Furthermore, the information is often imprecise and will include both qualitative and quantitative elements. For these reasons it is important to extend traditional decision making processes by adding intuitive reasoning and human subjectivity. Furthermore, using artificial intelligence technology for realizing above-mentioned concepts, fuzzy logic can be regarded as a tool of maneuvering imprecise information. If one can combining both the fuzzy method with the AI technique for forming “Fuzzy-AI” model (Lin, 1998, 2008), where appropriate concepts, software tools and languages could build a powerful instrument for deepening, analyzing and controlling the sophisticated real world problems. This section presents the philosophical basis of “Quantitative Management”, discusses the principles and methods of the theorem based on subjective information. It raises the question of why a practical social or engineering problem can be solved by the concepts of systematical investigation and fuzzy treatment. On the other hand, the decision making in management can be maneuvered by subjective fuzzy information incorporated with AI simulation. Through philosophical reasoning, it is evident that the theoretical foundation of “Quantitative Management” is derived logically by the expression of fuzzy mathematics within the framework of error tolerance in management. Based on engineering methodology by subjective information, “Fuzzy-AI” Model explored the essentials of solving strategy for real world problems—systematic representation by qualitative analyses and then fuzzy-AI simulation for quantitative solution. So-called “artificial intelligence” could be understood as the arts of simulation of human intelligence by computer; thus it is a natural deduction for processing managerial science problem by means of “Fuzzy-AI” model. The final resolution of real-world problems under uncertain environment is rather complicated and has taking serious concerns by practitioners and it can be effectively solved through the combination of qualitative-quantitative approaches. Series of practical problem analyses show that there is a possibility to explore a new

1.3 Philosophical Considerations of “System-Fuzzy” Model

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management discipline called “Fuzzy Quantitative Management” based on “FuzzyAI” model for solving wide-ranged management problems qualitatively through systematic method; and quantitatively through fuzzy approaches. A series of real world problems have been solved as following: Intelligent engineering design; Economic analysis of oil fields; Quantitative project management problems; Price decision making under competitive market; Quantitative risk assessment of project management; etc. Further investigation shows that the above-mentioned problems are solved mostly through the methodology of “System-Fuzzy” approach, which means when dealing with complicated problem, first of all, one needs to perform strictly systematical analysis for qualitatively defining its nature; then for quantitative solution, it could be flexibly treated through fuzzy approach. It is emphasized that the absolute optimum solution doesn’t exist and also not realistic in the real world, in the contrast, under VUCA era, the only realistic solution is the “acceptable” solution, “reasonable” solution or so-called “satisfactory” solution. It is due to “System-Fuzzy” model, “Fuzzy-AI” model and sensitivity analysis (Lin, 1990), the philosophical and theoretical background of “Fuzzy Quantitative Management” are based.

1.3 Philosophical Considerations of “System-Fuzzy” Model The philosophical and theoretical foundation of fuzzy quantitative management is based on “System-Fuzzy” model and “Fuzzy-AI” model. Besides, the fuzzy membership function will not be selected arbitrary; theoretically, the appropriate selection of membership function in fuzzy inference can be solved by AI and machine learning (Lin, 2005a), thus the theoretical argument of uncertain decision for quantitative management is setting up. The objective world is full of uncertainties. Due to the limitation of human subjective information and the complexity of the connection between events, certainly it will be increasing one’s misunderstanding in maneuvering the internal essentials and development tendency of the events. It must be noticed that in managing complex events, three points should pay attention as below: Firstly, one needs to emphasize the global situation and mastering the general development tendency; Secondly, based on system engineering ideology, one needs carefully to subdivide various factors which influenced the development of events, through “factor tree” for finding out the key problems and main contradictions for its solving,; Thirdly, correctly solve the qualitative and quantitative relation of the problem, then combined the methods of qualitative and quantitative solution through semi-empirical and theoretical formulations. When a practical complex problem is encountered, one needs to catch global development of the system and abandon the less essential and less important issues;

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such as in conceptual design stage of structure, the designer has to catch the general information of the tasks of the structure for forming the structural framework and neglecting the structural details. The designer(s) should concentrate their attentions on the mechanical behavior of structure in resisting the external loading conditions for maintaining its normal working status; so as the design of complex structural system can be easily understood and easily to maneuver and expressing the design idea explicitly. Since the emergence of probability theory, which applied to uncertain flood prediction problem and obtained favorable result; nevertheless, due to the limitation of people’s knowledge in theorem, they improperly regarded the probability theorem can solve every uncertain problem, this is an one-sided misunderstanding of the objective events. It is emphasized that the uncertainties of events are different with each others, such as: uncertainty in occurrence; uncertain in degree of performance as well as uncertain in the distance of event performance with its prediction, etc. Thus, the mathematical modeling of above-mentioned cases must be different, such as: Probability modeling is suitable for modifying uncertain in occurrence problems; fuzzy sets or grey theorem are properly used to those performance uncertain events, etc. It is improper to arbitrarily use whatever theory for modeling the uncertain events without consideration of the “Fitness” of its physical nature. Facing to big amount of “non-structured” problems in the objective world, which cannot be expressed by analytic mathematical formulation and cannot be solved by analytic solution, one should avoid to those logic inferences and constrain one-self in tedious formulation framework during its modeling stage; on the contrast, one should fully utilize the empirical judgment and capacity of fuzzy information processing of human being to fully extend the roles of powerful “subjective information” in the modeling process. As a matter of fact, management is essentially to make a series of decision for certain objectives and under uncertain environment; therefore, whether it is uncertain in occurrence or uncertain in degree, the decision making during management should fully consider the nature of the uncertainty. However, it’s been a long time that there is a “Paradox” in dealing with the “uncertain nature” in managerial event; that is come from the popularly using probabilistic method and neglecting to the compatibility with its physical nature. Since the managerial event is an objective existence; the uncertain nature of which is the degree of its performance rather than its probability of occurrence. The “Paradox” of applicability of the proper theory to its accommodating problem nature is just an initiative understanding problem of human being in their cognition to the development of objective world; so it can be clarified through deepening the investigation of the essentials of the problems. We are living in an uncertain systematic world, for every event around us no matter economic, social or engineering technology, etc. is an uncertain system problem in nature. What we are doing is just recognition, evaluation, analyses and making decision for these uncertain system problems; the problem solving is based on long term cumulated empirical experiences of human being and fuzzy information processing. Therefore, applying system science concept to define the problem outlook and establish the problem solving framework; then using fuzzy theoretical modeling for finding

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“acceptable”, “satisfactory” or “reasonable” solution, will be a wide and prospective direction in problem solving and also in the understanding of cognition science.

1.4 Philosophical Considerations of “Quantitative Management” It is no doubt on the fact that the development of science and technology will be more and more depended on the development of computer technology. As to the development of computer science, it is ever increasing the tendency that the position of artificial intelligence (AI) will be ever evident than that of computer networking and computer integration. Especially in the domain of complicated “non-structured” problem solving, the applications of AI technology will have its broad space of development. As we know AI can be simply illustrated as the technique of simulating intelligence of human being by computer; without solid and correct understanding to the objective world by human being, it is by no mean to say the solid basis of “intelligence” of the people. Therefore, the “Paradox” of understanding that we discussed in Sect. 1.2, will definitely influence to the development of AI, so as to constrain the solving of “non-structured” problems. The importance of this philosophical law cannot be underestimated, which will directly influence to acquaint oneself to the recognition the objective world and reform the objective world of human being. It is understood that (Ji, 1994) the reliability of structure is a problem of “degree uncertainty”, based on the “factor tree” method in system theorem, the whole structure can be subdivided into several parts and each part will further divide into structural elements; then the “Fuzzy Analytic Hierarchy Process (FAHP)” model of structure can be setting up with fuzzy reliability model and corresponding weights for each structural parts, for quantitatively evaluation of the reliability of structures. Fuzzy model is congenital to the subjective information of human being, it is a powerful tool, which connected to AI technology can be widely apply to economic, social, science and technology problems for its solution. Moreover, the fuzzy model can be further used for “Data Mining”, for deepening the “shallow data” to “deep data”, and from “shallow knowledge” to “deep knowledge” (Lin, 1991). The powerful tool with combination of fuzzy inference and AI technology—the “Fuzzy-AI Model” was then suggested, which has been successfully used in the investment decision, economic assessment, system planning, system control, system reliability and safety assessment, structural strength, intelligent design and grade evaluation, etc. Though the fuzzy method has recognized by people in its qualitative assessment, however, its quantitative assessment for the events still remains questionable. The fundamental reason of which is that the selection of fuzzy membership function for assessment remains randomness without argument. For cover this shortage, we firstly analyze why the probability density function in probability theorem can be accepted? It is only because which is an analytic function satisfying derivative and convergence conditions, its parameters are obtained through statistic regression from

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the real observed data, so it fully reflect the reality. Similarly, if the fuzzy membership function can satisfy the some conditions of derivation and convergence, it will possess the same confidence as the probability density function. Obviously, since we may take analytical function to be the fuzzy membership function, the derivation feature can be easily satisfied; as regard to the convergence feature, we may determine the parameters in the fuzzy membership function through AI supervised learning of appropriate samples (Lin, 2005b) for its solution. Thus the argument of the applicability of fuzzy theoretical method in quantitative assessment can be verified. It is concluded that system science and fuzzy recognition are the big breakthrough of human being in understanding and reforming the objective world, it is far from clarified of its powerful influence to the objective world, for there are many fields in nature and human society, still waiting for exploring in the future. Mastering the theorem of system science, one may clarifying the direction of develop tendency of event, operate at the strategically high position, all rounded observed and solve the problem comprehensively. On the other hand, mastering the fuzzy recognition theory, one may apart from the ideological world and landing to the realistic world; abandon to strive the optimum solution rather than to persuade “acceptable” solution or “satisfactory” solution. To be “strive for realism and innovation”, having correct thinking philosophy, one may increasing his/her capacity of practical problem solving under such an uncertain environment. Any now idea and thinking philosophy in human society, the process of its acceptance may take a considerable time; it is doubtless for acceptance of the system science and fuzzy inference theorem by the people. However, we should affirm every progress in this regard, such as intelligent design and AI consideration, which essentially are based on system science and fuzzy recognition theorem. Applying fuzzy design space and inference to match the intelligent design have raised big interests in academic community. The characteristics of our era is “Softening of Theorem and Hardening of Knowledge” (Lin, 2002), especially under the VUCA and rapid development of IT technology era nowadays, the roaring out of the new discipline “Fuzzy Quantitative Management” is accommodating to the objective reality and it is natural and inevitable. Certainly, there are different expressions and theorem for “Quantitative Management”, we in this monograph is using fuzzy inference theorem, since fuzzy approach is powerful and flexible to different cases. Moreover, the problem can be unified to solve through “Fuzzy-AI Model” that is an effective approach for solving our problems.

1.5 Conclusive Remarks It is concluded that the new discipline of “Fuzzy Quantitative Management” is the product in digital era, while every discipline is facing to digital transformation. An efficient tool to simulate human intelligence for performing digitized decision making

References

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is the integrated fuzzy approach with AI to form the “Fuzzy-AI” model; which can effectively generate appropriate and reliable argument for decision making; thus forming the fundamental basis of quantitative management; in other word, forming the theoretical basis of “Fuzzy Quantitative Management”. The important roles of “subjective information” in the intelligent operations have fully extended in this chapter, since it has become increasingly difficult to analyze the huge amounts of qualitative and quantitative imprecise data and information, for these reason, it is important to extend traditional decision making process by adding intuitive reasoning, imprecision and human subjectivity, which is based on the idea of abandon to pursue optimization rather than that of “acceptable” or “satisfactory” solution. The initiation and growth of “Fuzzy Quantitative Management” is discussed in this chapter, it also analyzed the theoretical framework as well as the philosophical foundation. Since the majority of non-structured degree uncertain managerial problems have been treated by probabilistic approach, which is fundamentally not matching to its physical nature, by which it causes the “Paradox” problem in the decision making under uncertainties. For the majority of managerial problems are “degree uncertainty” problems, it is proper to be modeled and solved by fuzzy sets or grey theory. Even though plenty of intelligence has possessed by the management decision makers, but it is still not enough to deal with the severe changeable environment under VUCA era. Therefore, the new discipline “Fuzzy Quantitative Management” makes management to be networking, digitized and intelligentized possible, is the doubtless orientation of development in the future. To simulate intelligence of human being through computer, to manipulate information from different aspects through network communication, to transfer project risks from qualitative to quantitative through digitization, the techniques of fuzzy machine learning (Lin & Dong, 1998) and neural network (Lin & Xiao, 1998) will be fully applied, it will greatly promoting the development of managerial science. The “Fuzzy-AI model” presented in this chapter accommodating to each aspect of managerial events, provides an efficient practicing tool in “Quantitative Management”.

References Ji, Z., & Lin, S. (1994). Damage assessment system of structures based on fuzzy analytic hierarchy process. Computational Structural Mechanics, 11, 232–240 (in Chinese) Lin, S. (1990, August). On earthquake eccentric sensitivity behavior of structures. Tech. Report (2) under Contract NCEER-88-1006, Cornell University, U.S.A. Lin, S. (1991). A conceptual approach of fuzzy decision for systems by “deep knowledge” and “deep data”. In Proceedings of 2nd International Conference on the Application of AI Technologies in Civil and Structural Engineering, U.K. (1991). Lin, S. (1998). Fuzzy-AI model. In Proceedings of 2nd International Conference on AI and Its Applications, May 1998, Wuhan, China (pp. 56–72).

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Lin, S. (2002, November). Fuzzy-AI in design consideration. Lecture in Architectural Design Institute, Harvard University. Lin, S. (2005a). On paradox of fuzzy modeling: Supervised learning for rectifying fuzzy membership function. Artificial Intelligence Review, (23), 395–405. Lin, S. (2005b). On paradox of Fuzzy-AI modeling: Supervised learning for rectifying fuzzy membership function. Journals of Artificial Intelligence Reviews, 1, 395–405. Lin, S. (2008). “Fuzzy-AI” model for managerial science, Keynote speech in the plenary session of PMI World Research Congress, July 2008, Warsaw, Poland. Lin, S., & Dong, B. (1998). Modeling of fuzzy machine learning and fuzzy neuro-network in structural design. In B. M. Ayyub (Ed.), Uncertainty modeling and analysis in civil engineering (pp. 167–187). CRC Press. Lin, S., & Xiao, L. (1998). on convergence condition of neuron-network simulated by energy expression. In Proceedings of 2nd International Conference on AI and Its Applications, May 1998, Wuhan, China (pp. 290–297).

Chapter 2

“Fuzz-AI Model” for Quantitative Management

2.1 The Features of Quantitative Management The essential of management is: For definite purposes, using limited resource (time, manpower and asset) to make a series of decision under uncertain environment. In the past, management decision usually is made by project manager with his/her subjective judgment and subjective information to decide an action based on strategy instantaneously. However, what strategy is going to take? What action is later to follow up? Such process of decision making is characterized in its randomness, the decision quality is heavily depends upon the experiences and judgment of the decision maker, it is not a stable situation and the quality of decision cannot be guaranteed. For avoiding the disadvantages of decision making based in subjective information, one needs to using computer and AI technology to support the decision processes. Then, digitization is the pre-requisite condition of such an operation; only by this way, the “Quantitative Management” can be arisen spontaneously. The function of quantitative management is that it can quantitatively evaluate all the performance of managerial attributes and thus support the managerial decision making of the event. Moreover, since all the processing is digitized and implemented by computer; so it can be stored in computer memory and become a part of data base or knowledge base for future application. For realizing quantitative management, there are two pre-requisites to be satisfied: the first one is how to establish uncertain mathematical model? And the second one is how to simulate human intelligence for management decision making? For abovementioned reasons, there must be a model satisfying these two conditions. On one side, the event must be illustrated by fuzzy inference accommodating to its uncertain environment; on the other side, the artificial intelligence must be used to simulate human intelligence for the decision making in the project events; thus, the “Fuzzy-AI Model” is emerged correspondingly for satisfying above-mentioned requirements.

© Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 S. Lin and G. Zhao, Fuzzy Quantitative Management, Fuzzy Management Methods, https://doi.org/10.1007/978-981-10-7688-6_2

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2.2 What So-Called “Fuzzy-AI Model”? First of all, we need to define the fuzzy method, which is a quantitative simulation of inference processes and represents the capacity of decision making of human being based on Fuzzy Sets theorem (Zadeh, 1965). It uses the concept of fuzzy membership function to maneuver the ambiguity of knowledge. There is an innate relationship between fuzzy method and artificial intelligence. The fuzzy inference represents the capability of human being facing objective phenomena (mainly degree uncertain event) to carry out induction, reasoning, deduction and conclusion, which are the main intelligence of human being. The fuzziness is the fundamental feature of events in objective world, which coincide the innate instinct of human being, people judge objective events by means of subjective information to make fuzzy inference. As the capacity of processing massive fuzzy information instantaneously by human being, which is in form of hierarchy (Saaty, 1980) can be simulated by the AI technology and processed through computer, then it will be possible to make managerial decision by computer. The “Fuzzy-AI Mode” (Lin, 1998, 2008) will make all of these possible to realize implementation of the processes. The construction of “Fuzzy-AI Model” is rather unique, it regards the state of objective event is decided by the states of several inter-related factors. Due to the state uncertainty of these factors the event will remain uncertain (Lin, 2008). The operation of “Fuzzy-AI Model” can be divided into two parts: (1) Fuzzy representation, fuzzy evaluation and fuzzy demarcation for the state of each uncertain factor; (2) The intelligent inference (induction, reasoning, deduction and conclusion) is carried out to each fuzzy expressed factor state; then the fuzzy represented uncertain state of objective event can be obtained, which can be used as the argument of decision making for objective events. The “Fuzzy-AI Model” can be understood as the combination of fuzzy inference with the involvement of human intelligence through AI technology; the previous one could use the practical tool of fuzzy inference to simulate the problem solving processes for obtaining “acceptable” and “satisfactory” solutions. The latter one incorporates the application of AI technology, which representing the involvement of human intelligence (mainly subjective information) that can be expressed by mathematical modeling (such as fuzzy inference model). Thus, the function of the combination will show powerful capability to solve various real world complicated problems.

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2.3 Theoretical Foundation 2.3.1 The Meaningfulness of Fuzzy-AI Model The most meaningfulness of “Fuzzy-AI Model” is that it explores the channel for representing implicit information from one’s mind (or non-structured complicated event) and makes it explicitly digital accessible, that will fundamentally solve the problem of retrieving implicit information from one’s mind (or objective realistic event) to contribute for the completeness of source information for “Big Data” searching. It also explores a new approach for solving real world “non-structured” problems. It’s understandable that people uses mathematical modeling for real world complex problem solving. The merit of modeling is that it reflects the internal objective law of the event to be studied, then no matter who and at what manner the operator is, the result will be the same and to be convergent. It is also valid even though in those in-deterministic problems from probabilistic to random events. Nevertheless, the majority of real world problems in economic, social, engineering, natural and environmental phenomena are so sophisticated and it cannot be expressed and solved by analytic mathematical models. Those “non-structured” problems are difficult to solve simply by analytic modeling; that is why many social, management and humanity problems nowadays can even use the “subjective information” from human experience and intelligence for approximate assessment. Certainly the errors from subjective judgment could be correspondingly controlled by certain rules, such as “brain storm”. Due to lake of mathematical strictness in fuzzy set, even though the emergence of “Fuzzy-AI Model” is trying to modify the processes in human thinking by means of fuzzy digitization, it has not yet recognized by the public community as an efficient solving tool for the complex uncertain problems. It is still a bottleneck of build a reasonable fuzzy membership function until the reveal of using AI supervised learning from practical samples. The characteristics and academic meaning of “Fuzzy-AI Model” presented in this chapter can be concluded as following: (1) “Fuzzy-AI Model” provides the possible approach of using fuzziness to modify the intelligence of human being in digital form, which contributes to the completeness of source data and source information for “Big Data” searching, and which related to crucial foundation of its completeness and effectiveness; (2) Modeling the AHP (Analytic Hierarchy Process) incorporated with fuzzy sets and extending AHP to FAHP model of in-deterministic fields; (3) Building reasonable reality-based fuzzy membership function by using AI supervised learning of practical samples for moving bottleneck of applying fuzzy approach in practice;

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(4) Numerous practical problems in different fields of application were solved by “Fuzzy-AI Model”, such as in engineering, economics, social, management, design and soft science, also in the fields of infrastructures, financial development, transportation, logistics, etc. (Lin, 2014). The “Fuzzy-AI Model” technique is targeting on removing the insufficiency of pure fuzzy mathematics in solving practical problems. By means of problem-oriented strategy to study the applicable problem-solving models without rigorous and tedious pure mathematical verification, the “Fuzzy-AI Model” can also be classified in different reasoning models. The AI technology combined with fuzzy reasoning can be engaged in different models for solving problem in a variety of particular features.

2.3.2 Setting of “Fuzzy-AI Model” The hereditary relation between fuzzy information and human intelligence will innately promote the combination of “Fuzzy” and “AI”, which is recognized as the simulation of fuzzy information processing capabilities of the human being. The essential of the “Fuzzy-AI Model” is to transfer the implicit vague information of non-structured problems to explicit fuzzy algorithmic expressions that are digitized and computable for problem solving. The key of this transformation is the fuzzy membership function, which quantifies the vague information into computable quantities for evaluation. The generalized factor (attribute) space is considered as the basis of the model, which is the carrier of all kinds of information and is depicted hierarchically by the attributes of the event either by a network or a tree structure shown Fig. 2.1. For simplify reason, the generalized factor space model is illustrated by a two layered hierarchical spaces shown in Fig. 2.1. For top level we define generalized factor space ∏ = {∏i } (i = 1, 2, … n), and for lower level factor sub-space, we have ∏i = {lik } (k = 1, 2, …, ki ). The global state of event T is defined by the states of its attributes ti

T

l11........ l1j.............

t1..............

.................ti.........................................

........ l1m1

li1.........

lik..................

Fig. 2.1 Two layered hierarchical attribute space

.. liki

...........tn

ln1........ lnp.. ...

lnkn

2.3 Theoretical Foundation

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T = F(t1 , t2 , . . . , ti , . . . , tn )

(2.1)

The state of attribute ti (i = 1, 2, …, n) can also defined by sub-attributes ti = Fi (li1 , li2 , . . . , lik , . . . , liki )

(2.2)

Figure 2.1 is just the simplest form of tree structure of generalized factor space concept. For specific issue, the state could be expressed in its factor space ∏ constructed by its subspace ∏i other than tree structure. We may take the investment of engineering project decision making as an example: T: Investment project, which is determined by its main attribute t1 , t2 , …; where t1 : Economic profitability of project, which depends upon its attributes. l11 : Total engineering cost. l12 : Annual revenue of project. … t2 : Technical availability of the project. l21 : Difficulties in planning. l22 : Difficulties in design and construction. … t3 : Political and environmental aspects. … Note: State of top level space is expressed by ∏. States in lower level subspace is expressed by ∏i . State parameters for determining ti are expressed by lik (i = 1, 2, …, n; k = 1, 2, …, ki ).

2.3.3 Fuzzy Distance with Variation Weights The difference between two events ∼ A and B in its i-th attributive aspect can be ∼ measured as the distance di (A , B ) between them, the final distance D( ∼ A, B ) is ∼ ∼ ∼ presented by the summation of each individual di in m-dimensional space with corresponding weight coefficient wi (di ): D(A , B) = ∼ ∼

m ∑

wi (di ) ∗ di ( A, B)

(2.3)

i=1

In Eq. (2.3), wi (di ) might not be constant, it changes with certain penalty function pi (di ).

14

2 “Fuzz-AI Model” for Quantitative Management

wi (di ) = ai ∗ pi (di ) where original ai , weight of the i-th attribute,

∑k i=1

(2.4)

ai = 1.0 and

| ( )| | | A, B pi (di ) = 1 + |di ∼ | ∼

(2.5)

Equation (2.5) shows that the larger the absolute value of distance between ∼ A and B , the larger the amplified coefficient pi (di ), and then the larger the distance D( ∼ A, ∼ B ). ∼

2.3.4 Fuzzy Distance by Nearness Degree Nearness degree is accounted by fuzzy theory. Suppose there are n samples A1 , A2 , …, An ; the attribute of which is defined by gi (i = 1, 2, …, m) representing the main factors of event to be considered. { } G = {g1 , g2 , . . . , gm } = g j

(2.6)

As an example of assessment of an earthquake resistant building, G could be expressed by a series of frame parameters. g1 number of stories. g2 height of the building. g3 total base shear. … gm total lateral stiffness of unit framework. Denote G the fuzzy subset of i-th sample Ai to the attribute gj ∼ ij

j

G = ∼ ij

j j j μiq μ μ μi1 + i2 + · · · + + · · · + ik = g1 j g2 j gq j gk j

⎧

μi g pj

⎫ (2.7)

where μiiq is the membership function of i-th sample Ai to q-th grade gqj of j-th attribute gj of the event. The nearness degree can be obtained by following steps: If ∼ A and B are two fuzzy ∼ subsets on universe X, then the interior product and exterior product of ∼ A and B can ∼ be expressed by Ang and Tang (2007): √ √ μ A ʘ B = ∨ μ (X ) (X ) ∧ A B ∼ ∼ ∼ ∼ x∈X

(2.8)

2.3 Theoretical Foundation

15

√ √ μ A ⊗ B = ∧ μ (X ) (X ) ∨ A B ∼ ∼ ∼ ∼ x∈X

(2.9)

The nearness degree of ∼ A and B is ∼ )] ) 1[ ( ( A = N ∼ A, B ⊗ B + 1 − A ʘ B ∼ ∼ ∼ 2 ∼ ∼

(2.10)

Substituting Eqs. (2.6) and (2.7) to Eqs. (2.8) and (2.9) then to Eq. (2.10), one may determine the set of nearness degree ( ki j =

A ,B ∼ ∼ i

j

) , ( j = 1, 2, . . . , m; i = 1, 2, . . . , n)

(2.11)

For specific attribute gi , the top three nearness degree over n samples Ai (i = 1, 2, …, n) are k1j , k2j , k3j , the fuzzy estimation ε vector for fuzzy information are: [ ( ) ( )( ) ε j = λ j k1 j ∗ E 1 j + k2 j ∗ E 2 j 1 − k1 j + k3 j ∗ E 3 j 1 − k1 j 1 − k2 j ( )( )( )( )] + E 1 j + E 2 j + E 3 j 1 − k1 j 1 − k2 j 1 − k3 j (2.12) { } ε = ε j , ( j = 1, 2, . . . , m)

(2.13)

where εj , the j-th component of estimation vector ε corresponding to j-th attribute gj . λj , the empirical coefficient of rectification, which influences the estimation and can be chosen through test. E1j , E2j , E3j , the value of samples with respect to attribute gj corresponding to k1j , k2j , k3j . In calculation, λj is suggested as 0.9 to 1.1. Usually, λj is determined by extensive statistical data processing and comprehensive studies based on the data of existing samples. The fuzzy solution based on existing samples can be obtained once the particular attributes are defined. Equation (2.12) represents the searching processes of those states which are most near-by to the current design. It is also mentioned that λj can be determined by self-training, i.e., it can be tested by trial-and-error method; input the sampling values and try to obtain the expected output.

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2 “Fuzz-AI Model” for Quantitative Management

2.4 Decision of Long-Term Highway Maintenance Investment The application of Fuzzy-AI model in practice can be shown in following sample of decision of long-term highway maintenance investment, it is illustrated below:

2.4.1 The Introducing of Pavement Damage State Indicator, PDSI Since people almost in every time may find there are many imperfections and damages on the surface of highway in different degree, which are caused by different reasons such as traffic overloading, complicated environmental conditions, road materials, construction conditions and other road structural problems. Therefore, it is necessary to have annual investment for maintenance of the highway network in order to guaranteeing the normal working state of the highway system. The following problems have to be studied: (1) What is the annual maintenance distribution principle of highway system? (2) How can an optimum annual budget distribution of maintenance investment for the highway system in long-term? For solving above-mentioned problems, we introduce the concept of “Pavement Damage State Indicator, PDSI” for representing the state of damage degree of the highway surface, which combines by different damage factors and can be used directly to the possibility of normal operation of the highway system. According to the distribution of PDSI along the highway system, one may determine the distribution of annual maintenance investment along the highway system (network); moreover, for the purpose of considering long-term system efficiency of maintenance investment, we also need to analyze and optimize the long-term investment of highway system.

2.4.2 Fuzzy Model for the Decision of Highway Maintenance Investment We introduce fuzzy model for the solving of long-term highway maintenance investment, it will related to how to select the fuzzy membership functions for quantizing the technical damage factors of the highway system, such as surface cracking, unevenness, etc. so as the solving of problems with reasonable and correct solution. The purpose of introducing fuzzy model is that it makes possible of the observed uncertain damage degree of the highway system to be quantized.

2.4 Decision of Long-Term Highway Maintenance Investment

17

μ ( ti )

Fig. 2.2 Failure state of the membership function

1.0 0.9 0.81 cr

0

ti / t i 0.5 0.6

1.0

According to above illustration, the evaluation of technical state of highway system can be concluded as the distribution of pavement damage state indicator PDSI in each sector throughout the whole highway system. Define S represents the state of PDSI, we have: S = F(t1 , t2 , . . . , tm ) = F(ti )

(2.14)

where ti (i = 1, 2, …, m) is the attributes of S the high way system, such as cracking, unevenness, etc. Suppose any ti approaches to its critical value ticr , then fuzzy model can express its failure state. As shown in Fig. 2.2 when μ(i) f = 1, it must be notified that the fuzzy membership function shown in Fig. 2.2 is just preliminary or tentative, it will be improved by means of machine learning later. In order to express the system characteristics of the A and B : highway network, we introduce the concept of distance D between states ∼ ∼ (

)

= D ∼ A, B ∼

m ∑

wi ∗ di ( A, B)

(2.15)

i=1

where m—the dimension number of highway system state ∼ A and B ; ∼ wi —the weight of i-th attribute. ( ) | ⎧ ( ) | di ( if d i < di < d i ) | | = A, B = di ∼ (t ) − μ (t ) |μ∼A i B i | ∼ ∼ 0 if di ≥ d i or di ≤ d i

(2.16)

where μ∼A (ti ) and μ∼B (ti ) represent the state values of fuzzy membership function of the A and B . i-th attributes of ∼ ∼ d i and d i represent the upper and lower bound of di .

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2 “Fuzz-AI Model” for Quantitative Management

Hi

ζi

1.0 ai

1.0

di

0

di

0 1.0

Fig. 2.3 The expending function Hi and penalty function ζi

The i-th attribute and its variation functions of weight factor ai are shown in (2.17): wi (di ) =(Hi (ai , di ) ∗ ζi)(di ) ζi (di ) = 1 −(2di + di2 (

Hi (ai , di ) = (1 − ai )exp −

⎫ ⎪ ⎬ di2 p

)

)⎪ + ai ⎭

(2.17)

here Hi —The expanding function of critical state; ζi —The penalty function of fuzzy distance as shown in 2.3; ∑Fig. m p—expanding factor, p = 0.1 is an empirical number, i=1 ai (i = 1, 2, …, m) = 1. It is obvious that from Fig. 2.3 that if di vanishes, then ζi (di ) and Hi (ai , di ) approach to 1; However, if di approaches to 1, then ζi (di ) tends to zero. In Fig. 2.3 Hi curve represents the increasing of weight ai of the i-th attribute ti ; when di tends to 0, then Hi approaches to 1.0; The penalty function ζi includes two cases; when ζi tends 1.0, then di approaches to 0; and when ζi vanishes, ti locates in normal state. The fuzzy distance between state ∼ A and B can be expressed by the values of ∼ fuzzy membership functions μ∼A and μ∼B . For example, when the critical state of i-th ( ) A ∼

( ) B ∼

( ) A ∼

( ) B ∼

attribute = 100, ti = 50 and ti = 60, parameter (ti and (ti /ticr ) are 0.50 and 0.60 respectively, then we have μ∼A = 0.81 and μ∼B = 0.90; finally, we ( ) A, B = 0.09. It is cleared that the fuzzy distance D is the comprehension of obtain di ∼ ∼ individual fuzzy sub-distance di . In practice, the fuzzy membership function of most concerned attributes cannot be exactly determined by heuristic knowledge of domain experts; under such circumstance, the selection of fuzzy membership function and its further improvement of these attributes can be realized by the supervised machine learning of carefully selected samples. In order to quantize S in (2.14), one needs to provide precised value of fuzzy membership functions for each attribute ti (i = 1, 2, …., m) μi ; As a matter of ticr

/ticr )

2.4 Decision of Long-Term Highway Maintenance Investment

19

fact, S is just involved few attributes, its di satisfies the first condition of Eq. (2.16), therefore, we have: ⎫ (The system is located above abnormal critical state) ⎬ S>S S < S < S (The system is located in normal working state) ⎭ S S G , it represents the severe damage of the pavement surface, the traffic must be terminated; (2) When S G > SG > S G it represents some damages of the pavement surface are found, but the traffic can be taken with local maintenance investment ΔS; (3) When SG < S G ; it represents the slight damage of the pavement surface, traffic can be continued without notice. Figure 2.6 also makes the highway manager understand how to planning the direction and intensity of the maintenance investment for maintaining the normal SG state and corresponding PDSI in each sector of the highway system. It is the very basis of planning the annual and long-term maintenance investment and also the very basis of consultation and decision making of the operation in damaged highway system. In this example, the PDSI is effectively modeled by n-dimensional ellipsoid; it is obvious that based on these principles (PMBOK Guide 2020) with well selection of fuzzy membership functions (Lin, 2005), the model also can be widely applied to other n-attributes problems.

2.5 Conclusive Remarks It is understandable that the realization of so-called “Quantitative Management” must satisfy three conditions, i.e.: digitized operation, necessary tolerance in management and involvement of artificial intelligence into decision making process. The first condition digital operation is pre-requisite factor, since the operation is carried out on computer, every managerial factor and attribute must be expressed in digital form; no matter it could be in statistic or probability forms (Ang & Tang, 2007), the second condition is concerned to “necessary tolerance”, which is obvious needed for management, since management is a process of making a series of decision under uncertainties, any uncertainty must associate with tolerance. In this point, fuzzy inference could fully involve this tolerance (Zirmmermann, 1987); the third condition the involvement of AI technology is the direction and tendency of scientific development, which can be greatly increasing the management in its automation and efficiency. We conclude above mentioned points that the application of “Fuzzy-AI Model” can be suitable to realize above three conditions of digital, tolerance and AI, therefore, it may widely apply to solve a family of non-structured management problems. In the example of maintenance investment of damaged highway system, “FuzzyAI Model” is firstly used for fuzzy expression of pavement damage state indicator PDSI in each sector. The general state of damaged pavement SG is the weighted average of different PDSI in each sector. The general damage state of whole highway system SG then becomes the argument of decision in maintenance investment of the damaged highway system. The methodology in this sample can be extended to similar cases of complicated real world problems.

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References A guide to the Project Management Body of Knowledge (PMBOK Guide) (2020), 6th Ed., 7th Ed. Ang, A.H.-S., & Tang, W. H. (2007). Probability concepts in engineering—Emphasis on applications in civil and environmental engineering. Wiley Inc. Lin, S. (1998). Fuzzy-AI Model. In Proceedings of 2nd International Conference on Artificial Intelligence for Engineering (pp. 56–72), May, Wuhan, China. Lin, S. (2005). On paradox of fuzzy modeling: supervised learning for rectifying fuzzy membership function. Artificial Intelligence Review, 23, 395–405. Lin, S. (2008). “Fuzzy-AI Model” for managerial science. In Plenary Session Speech and Proceedings of 4th PMI Research Conference, 13–16 July, Warsaw, Poland. Lin, S. (2014). The principle and methods of quantitative management—Fuzzy-AI Model in managerial science. Taiwan Project Management Network Co. Ltd., December (in Chinese) http://www. dooland.com/book/22286 Lin, S., & Yang, X. (1995). Intelligent data base supported system reliability control by successive precision of machine learning. In Proceedings of International Symposium on Uncertainty Modeling and Analysis, ISUMA’95, September, Maryland, USA. Saaty, T. L. (1980). The analytic hierarchy process, planning, priority setting, resource allocation. McGraw Hill, Inc. Wang, P. (1983). Fuzzy sets theory and its applications. Shanghai Press of Science and Technology (in Chinese). Yang X., & Lin, S. (1995). Long-term highway system investment decision based on fuzzy model of comprehensive pavement distress. In Proceedings of ISUMA ’95, September. Zadeh, L. A. (1965). Fuzzy sets, information and control (Vol. 8, pp. 338–353). Zirmmermann, H. J. (1987). Fuzzy sets, decision making, and expert systems. Kluwer Academic Publishers.

Chapter 3

The Conceptual Investigation of “Deep Data” and “Deep Knowledge”

3.1 “Deep Data/Knowledge” in a Decision System The conventional data base (DB) and knowledge base (KB) attached to conventional decision supporting system (DSS) will not be sufficient to the needs of decision making in daily encountered management problem, for all the data/information are “shallow data” and “shallow knowledge”, which cannot reflect the deep layer relationship of the event and being puzzled to decision maker during decision process. Therefore, in order to obtain deep data and deep knowledge, one needs to deal with the reasoning and decision processes which are represented by fuzzy nearness degree and fuzzy similarity degree in a decision state space by “digging” the deep data and deep knowledge (Lin Shaopei: Fuzzy reasoning method—Its theoretical adaptability & code compatibility to design of earthquake-resistant structure”, 1990; Shaopei, 1991). Some developments in this regard are presented based on hierarchical sub spaces of state parameters of the decision event. Promoted by the development of science and technology, it is an ever competitive and challenged environment for decision making, a system which can provide thorough-reaching explicit and implicit information is urgently needed. The gap between available information with what is expected in real world decision making is widening; it is due to the fact that there is a misunderstanding which leads the computer science people pay their attention in extending a DB system capability of manipulating data forms and data organizations explicitly rather than in improving its quality in maneuvering implicit data and information. It seems the time for us to rectify this tendency, common efforts should be paid through inter-disciplinary cooperation for improving the situation. It is understandable that different attributes of data are required by people in different post. A professional staff concerns only those data which are directly related to his (her) department. However, for department head, who will concern data much in the inter-departmental nature. Eventually, for the General Manager of a firm, who are

© Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 S. Lin and G. Zhao, Fuzzy Quantitative Management, Fuzzy Management Methods, https://doi.org/10.1007/978-981-10-7688-6_3

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3 The Conceptual Investigation of “Deep Data” and “Deep Knowledge”

responsible to the strategic planning, will pay his (her) concerns on more integrated information for decision making combined with his (her) personal experience and judgment. Certainly, the conventional, trivial and tedious “shallow data” and “shallow knowledge” will not be satisfied in this case. For an efficient decision supporting system (DSS), what is needed can be listed as below: • Capacity of information re-production supported to the decision making; • Availability of existing DB and KB technologies for the DSS with friendly users’ interfaces; • Potentiality of manipulating data-information-knowledge as a whole based on unified theoretical basis for enhancing and facilitating system capacity for decision making support. As a matter of fact, the essential of an efficient DSS is fully utilizing AI techniques to obtain deep data and deep knowledge for a sufficient and rational decision making. Method of representation and inference strategy are to be cardinally concerned, the previous one relates to the expression of a complicated causal event; however, the latter one pertains to problem solving by indirect searching or indirect reasoning. Therefore, AI can be likely concluded as to simulate the human intelligence for solving complicated real world problems through rational reasoning process. Eventually, the inference strategy of AI is to find a simplest approach from the initial state to the final objective state, which is not necessarily to be unique; however, among those feasible objective states, each one represents a feasible solution to the problem. What we’re concerned is the aims of AI strategy, which is to manipulate the movement of the intermediate states in order to achieve an optimum path of inference. A system aimed by its system goal, can be modeled by a set of factor constitutively structured by certain links between each other to form a factorial network which represents the essential of system behavior. The nodes of which represent the factors concerned, the weight distribution between factors located at the same level determined by the Eigen-value problem of “pair comparison” relation matrix (Yan, et al., 2022). Successively manipulating the weight distribution of the factor in each level until the fuzzy assessment of system response for decision making consultation is made. In order to explain the reasoning and learning processes in a complicated decision system, the hierarchical space of state is introduced for representing the internal relationship of the event in different level and in different aspect. Thus, the theoretical manipulation of reasoning could be performed in a unified approach explicitly in mathematical formulation. By means of hierarchical space of state, the network of fuzzy reasoning both for assessment and learning can be performed on a rather reasonable basis.

3.2 Representation of Hierarchical Space of State

25

3.2 Representation of Hierarchical Space of State For a complicated system, the mathematical model is based on the classification of event with different attributes, which behave in different structural models such as tree structure and network structure etc. The representation of knowledge or information of an event, no matter in production rule, semantic network or other framework, needs a media of its state description. The compositional hierarchy of the event implies the rationality of introducing the hierarchical space for representing and manipulating the inter-relationship of the sub-events in a hierarchical structure of the complicated event. Figure 3.1 shows a two-layered hierarchical space, where. ψ = {ti } represents top level space of state with i = 1, 2, …, n. ψ1 = {t1 } = l1j , …; ψi = {ti } = lik , …; ψn = {tn } = lnp , … represent lower level spaces of state with j = 1, 2, …nm1 , k = 1, 2, … nmi , and p = 1, 2, …nmn respectively. Therefore, ψ = {t1 ,t2 , …, tn } = (l11 , … l1j , … l1m1 ), … (li1 , … lik , … limi ), … (ln1 , … lnp , … lnmn ) with total dimension of n ∑

mh

(3.1)

h=1

The relation between top and lower states can be expressed by ( ) ψ1 = f 1 l11 , . . . , l1 j , . . . , l1m1 ψ2 = f 2 (l21 , . . . , l2c , . . . , l2m2 ) ····················· ψi = f i (li1 , . . . , lik , . . . , limi ) · · · · · · · · (· · · · · · · · · · · · · ) ψn = f n ln1 , . . . , lnp , . . . , lnmn or Ω = ( f1 , f2 , . . . , fi , . . . , fn )

Fig. 3.1 The hierarchy of state space

(3.2)

26

3 The Conceptual Investigation of “Deep Data” and “Deep Knowledge”

In state expression, for top space event. (It , Ot , G t ) i.e. → It Ot G t

(3.3)

for lower space sub-event (

) Il(i ) , Ol(i) , G l(i) → Il(i) Ol(i ) G l(i)

(3.4)

where: It and Gt are initial state and objective state of lower level sub-event. From (2) Ω is inter-level transform function for studying the operational relationship between top level operation Ot and the i-th lower level operation of the sub-event O(i) t , here we understand i = 1, 2, … n. It is possible to chart Fig. 3.1 as a network structure rather than a tree or having multi-layered hierarchy space of state if necessary.

3.3 Fuzzy Deep Knowledge Reasoning Approach 3.3.1 Mathematical Distance in Space Based on the space of state, the difference between two events A and B in the same attributive subspace can be measured by the generalized mathematical distance D. Define D( A, B) =

m ∑

wi (di )di (A, B)

(3.5)

i=1

where m, the dimension of (sub) space wi , influence coefficient of i-th factor (or i-th attribute). di , difference between A and B events in i-th attribute. It is obvious that D(A, B) = D(B, A) D(A, A) = D(B, B) = 0

(3.6)

In addition, we can extend the measurement of difference in (hierarchy) space to uncertain events; for instance: di (A, B) = μ A˜ (vi ) − μ B˜ (vi )

(3.7)

˜ and B˜ with where μA˜ (vi ) and μB˜ (vi ) are the membership function of fuzzy events A respect to its i-th attribute vi in the same (hierarchy) space.

3.3 Fuzzy Deep Knowledge Reasoning Approach

27

3.3.2 Fuzzy Hierarchy Reasoning Approach We now discuss the decision making process under uncertain environment. The decision is made taking consideration of several fuzzy attributes, which are both uncertain in quantity and its implicitness of relationship. If the decisive solution T (Fig. 3.1) is a particular state in its attribute space ψ, then according to (1) ψ is determined by its attribute state {ti } which can be further expressed by corresponding sub-attributes’ states in ljk . This situation holds true even in fuzzy representation. Thus, the process of decision making is transformed by a series of fuzzy state operation. (1) Space Chart Analysis Each attribute affected to decision solution T is presented in its own subspace ψi and is hierarchically through (2) influenced to the state of T in space ψ. The relation between ψ and ψi is shown in the space chart. The nodes of which represent the essential of the concerned factor subspaces associated with the state of the attributes (Fig. 3.2). (2) Fuzzy State Assessment Fuzzy state assessment for each attribute in corresponding subspace with respect to its root attribute is performed by evaluation matrix through “pair comparison” based on empirical prediction directly related to fuzzy reasoning process. Define five grade scales for “pair comparison” of state:

Goal level

Ψ: Evaluation of candidate projects for National Science Foundation NSF support

Criterion level

Indication level 1

Ψ1: Contribution to society Weight w1

Ψ’1: Practical meaningful Weight w’1

Ψ2: Competence cultivation Weight w2

Ψ’2: Scientific Ψ’3: Potential value Weight advantages w’2 Weight w’3

Ψ’4: Degree of difficulty Weight w’4

Ψ3: Project feasibility Weight w3

Ψ’5: Research duration Weight w’5

Ψ’6: Budget support Weight w’6

Indication level 2 Ψ’’1: Economic Ψ’’2: Social revenue Weight w’’1

revenue Weight w’’2

Decision level D1: Encourage 120% fund

D2: Accept 100% fund

D3: Decline 80% fund

Fig. 3.2 The hierarchy of state space chart analysis

D4: Amalgamate D5: Postpone 30-70% fund

D6: Reject

28

3 The Conceptual Investigation of “Deep Data” and “Deep Knowledge”

“1” represents that the state is slightly important to the system goal of decision; “2” represents moderate important; “3” represents obviously important; “4”, seriously important and. “5”, extremely important. This 1–5 grade scale is feasible to quantize the conceptual thinking of professional experts to make judgment for evaluating the state status of attributes and formulating evaluation matrix. ( ) ( ) A = ati j = (ζli )/ ζl j

(3.8)

where i, j are the attributive subspace order of lower level attributes; t, l mean the abbreviations of top and lower. ζli andζlj mean the membership function of i-th and jth subspaces of lower level with respect to its root space. For instance, for evaluation of candidate projects for National Science Foundation NSF support, in the indication level 1 of Fig. 3.2, we have: (a45 ) = (ζψ4 )/(ζψ5 ) = 3/1 = 3 it means that the important of attribute 4 (degree of difficulty) and 5 (research duration) with respect to its root (project feasibility) is obvious important “3” and slightly important “1”.

3.3.3 Weight Distribution Between Attributes It is necessary to weight the attributes in the same level. Simulating this problem as sequencing physical weights of a set of body without a scale, we may get it if the total weight of body 1, 2, …, n as well as each of its weight ratio rij = wi /wj (i,j = 1, 2, … ,n) are known. Suppose w1 , w2 , …, wn are the weight of body 1, 2, …, n respectively, then we can set up a weight ratio matrix A, where we have: ( ) A = ai j where ai j = 1/ai j aii = a j j = 1 or

(3.9)

3.3 Fuzzy Deep Knowledge Reasoning Approach

⎡w ⎢ ⎢ A=⎢ ⎣

1 , w1 , . . . . . . , w1 w2 w2 w2 , ,......, w1 w2

29 w1 wn w2 wn

............ wn wn n , ,......, w w1 w2 wn

⎤ ⎥ ⎥ ⎥ ⎦

(3.10)

Multiplying weight vector W AW = {nw 1 , nw 2 , . . . , . . . , nw n }T = nw

(3.11)

(A − n I ) = 0

(3.12)

Equation (3.12) means the weight vector is the Eigen-value problem of “pair comparison” matrix A with respect to Eigen value n, the Eigen-value reflects the weight distribution. i.e., the sequencing of weight distribution among attributes can be handled as an Eigen-value problem of Eq. (3.12).

3.3.4 Illustrative Example We have defined the space chart of the event to be decided and have assessed by fuzzy approach the state of its affected attributes as well as its weight distribution with the root attribute; then the fuzzy reasoning process can be carried out level by level. An example of evaluating candidate research projects for NSF fund support is studied hereby and the factor chart corresponding to space chart is presented in Fig. 3.2, which is comprised of 5 levels. Goal level space also serves for the decisive solution T = Ti (i = 1, 2, …, 6). There are sub-spaces in each level as indicated in Fig. 3.2 and the attributes associated with its weight are also noted. Since the state of any attributes is described in its own subspace interacted with the others upon its root attribute in the root space, therefore, the evaluation matrix A of each root attributes actually reflect the heuristic prediction of human knowledge and constitute a part of fuzzy reasoning process. The evaluation matrix Aψ i of each node in Fig. 3.2 with weight distribution are: ( ' )( ' ) w ψ1 ψ )(2 ' )i Aψ1 = 1 3 (ψ1 ) 0.75 ' 0.33 1 ψ2 0.25 ( ' )( ' )( ' ) ψ1 ψ2 ψ(3 w)i ⎛ ⎞ ' 1 0.2 0.33 (ψ1 ) 0.10 Aψ1 = ⎝ ' 5 1 3 ⎠ (ψ2 ) 0.64 ' ψ3 0.26 3 0.33 1 (

(3.13)

(3.14)

30

3 The Conceptual Investigation of “Deep Data” and “Deep Knowledge”

Aψ1

( ' )( ' )( ' )( ' ) ψ1 ψ2 ψ3 ψ w ⎛ ⎞(4 ' )i 1 1 3 2 (ψ1 ) 0.33 ' = ⎜ 1 3 2 ⎟ ⎜ 1 ⎟ (ψ2' ) 0.33 ⎝ 0.33 0.33 1 0.5 ⎠ ψ 0.10 ( 3' ) ψ4 0.24 0.5 0.5 2 1

(3.15)

The weight sequencing for subspaces ψ’i ( ') ( ') ( ') ψ1 ψ2 ψ3 w = 0.43 w = 0.14 w = 0.43 2 3 ( ') 1 ψ 0.75 0.10 0.00 ( 1' ) 0.25 0.64 0.00 (ψ2' ) 0.00 0.26 0.33 ψ ( 3' ) 0.00 0.00 0.33 ψ ( 4' ) 0.00 0.00 0.10 ψ ( 5' ) 0.00 0.00 0.24 ψ6

wi 0.34 0.20 0.18 0.14 0.04 0.10

(3.16)

The final decision can be achieved by fuzzy evaluation through fuzzy reasoning.

3.3.5 Fuzzy Decision Making The fuzzy decision making is based on evaluation of state of individual attribute in its own subspace ψ i . The global decision solution Di (i = 1, 2, …, n) in its decision space ψ. In Fig. 3.2, six alternatives are taken: D1 , encouraged project with 120% support funding; D2 , accepted project with 100% full funding; D3 , declined project with 80% support funding; D4 , amalgamated project with 30–70% supports; D5 , project postponed by certain reasons and D6, insufficient project being rejected. Membership function of individual attributes in ψ i ’subspace can be obtained by sequencing the weight distribution w’i with respect to Di (see Fig. 3.2). For which fuzzy evaluation matrix Aψ i (as (13) to (15)) should be formulated as ) ( ) ( ' ' ' ' ' ' Aψk = (ζik )/ ζ jk , i, j = D1, D2, D3, D4, D5, D6; k = w1 , w2 , w3 , w4 , w5 , w6 (3.17) Here: (ζik )/(ζjk ) represents the ratio of belonging of the solution Di and Dj with respect to k-th attribute in ψ k space. Substituting (3.17) to (3.12), a set of Eigen-vector can be obtained '

'

'

μk {μ1k , μ2k , . . . . . . , μ6k }, (k = w1 , w2 , . . . . . . , w6 )

(3.18)

3.4 Fuzzy Decision in Bidding

31

Equation (3.18) is belonging vector of the attribute k to the decision solution Di, which exactly is the membership function of k to Di. i.e., μ2k μ6k μ1k + + ··· + D1 D2 D6

μk˜ (Di ) =

(3.19)

The fuzzy decision should be made by Dm satisfying ( 6 ) ∑ μm = Max. μik , (i = 1, 2, . . . , 6; 1 ≤ m ≤ 6)

(3.20)

k=1 6 ∑

(μik ) = 1.0

(3.21)

k=1

where (3.20) means the maximum of summation of membership function of all attributes in ψ’k (k = 1, 2, … ,6) subspaces with respect to decisive solution Di; and (3.21) means the summation of membership function of attribute k in ψ’k subspace with respect to all decisive solution Di (i = 1, 2, … ,6) equals to unity. Equations (3.12), (3.18), (3.19), (3.20) and (3.21) represent the essentials of fuzzy reasoning for decision.

3.4 Fuzzy Decision in Bidding 3.4.1 Problem Illustration An example of decision making for bidding submitted by project contractor and supported by an intelligent data base is presented for the purpose of illustrating the techniques of this chapter, in which the intelligent DB is inferred by fuzzy approach. In the construction market, the clients are offering tender and calling for bidding. In responding to the call the contractor will submit the bidding proposal for the project with quotation offer. As a matter of fact, the offering cost is a key issue in total bidding activity; since the higher the cost quoted, the less competitive the proposal will be; on the contrary, the lower the cost offered, the more the risk of the project implementation will face. One should as soon as possible to estimate the cost before the deadline date of the bid on the basis of available data from data base, in which the attributes and costs of existing buildings are stored. The involving of fuzzy reasoning to conventional DB represents a new era of incorporating AI with DB technologies, enabling further deepening the available data and knowledge. In other word, it implies a new scenario of intelligent DB concept, in which the deep data (or deep knowledge) can be drawn from the conventional DB by fuzzy inference machine.

32

3 The Conceptual Investigation of “Deep Data” and “Deep Knowledge”

3.4.2 Mathematical Modeling Supposing there are n engineering project samples A1 , A2 , …, An , and factor attribute set G has m elements G = {g1 , g2 , . . . , gm }

(3.22)

As the tall building is concerned, where. g1 represents the type of the structure. g2 represents the characteristics of structure. g3 , stories of the building. g4 , storey height. g5 , architectural combination. g6 , type of interior decoration. g7 , floor area in total. If G˜ i is the fuzzy subset of i-th building to the attribute set G gi2 gim gi1 + + ··· + G˜ i = g1 g2 gm

(3.23)

where gij is the j-th membership component of i-th building to subset G and sign “+” is by no mean of plus, but the meaning of “collection”. The fuzzy subset of building to be quoted A* is G* A G ∗A = g1∗ /g1 + g2∗ /g2 + · · · + gm∗ /gm

(3.24)

where g*j is the j-th membership component of A* to subset G. Now the question is focused on fuzzy estimation vector e*A of the attribute of the building to be quoted, which can be expressed by e∗A = M · λ[α1 E 1 + α2 E 2 (1 − α1 ) + α3 E 3 (1 − α1 )(1 − α2 ) +1/3(E 1 + E 2 + E 3 )(1 − α1 )(1 − α2 )(1 − α3 )]

(3.25)

where M, the total floor areas. λ, empirical coefficient of rectification, which influences to the base price of the quotation very much. α1 , α2, and α3 , the top three similarity degree (as shown in eq. (3.25)) or the top nearest three mathematical distance (as shown in eq. (3.5)) among existing building samples α1 , α2 , … αn . According to the principle of priority, it will serve as the basis of latter estimation. We have α1 ≥ α2 ≥ α3 . E1 , E2 and E3 are the corresponding estimation vectors of building project related to α1 , α2 , and α3 respectively. In practical calculation, λ is suggested as 0.9–1.1. Actually, λ is a multi-factor attributed value and is fluctuated by the condition of individual contractor/firms,

3.4 Fuzzy Decision in Bidding

33

market price of material and equipment, the prescribed goal of bidding and site environmental conditions etc. usually, λ is determined by extensive statistical data processing and studies based on the available data of existing buildings, which serve as the samples.

3.4.3 Solving Procedures (1) In the first step, one should identify the necessary attributes which reflect the overall characteristics of the building to be quoted. (2) Determining the approximate membership function of the building to be quoted to the attribution fuzzy set by subjective empirical prediction or by statistical data processing. The strategy is that, taking one or two existing buildings as sampling object, the membership function of which is considered as the normalized standard 1.0; then the other components could be estimated by subjective information. This procedure is going back-and-forth through judgment by (3.24) until a comparative fuzzy relation between expected and sampling buildings can be established as shown in Table 3.1, in which the necessary information about the fuzzy set of the expected building. ˜ (3) Based on Table 3.1, the similarity degree of two fuzzy set in the universe X, A ˜ and B can be expressed by ˜ B) ˜ = ( A, ˜ B ˜ = where A⊗

> x∈X

[μA˜ (x)




(3.26) μB˜ (x)] represent the

˜ and B. ˜ Select the top three similarity interior and exterior products of the fuzzy sets A degree under consideration with the corresponding estimations E1 , E2 , and E3 . (4) The base price for quotation in bidding proposal can be estimated through Eq. (3.25).

3.4.4 Case Studies Case 1: The quotation of a reinforced concrete framework building for bidding. (1) The building characteristic attribution set G = {g1 , g2 , g3 , …, g8 }. where g1 represents structural type; g2 represents foundation type; g3 represents the condition of exterior decoration;

SPF & SCS

Building C4

0.8

0.6

RCRF

0.5

0.8

1

0.6

CSM

Plate

Stone

Stone Mounting

Lime-Stone

CSM

Descriptions

0.55

1

0.5

0.5

Reltn. Coeff

Exterior decoration

Building

0.5

Mid. NS=13 HS=3.3m

Side NS=10

HS=3.6m

NS=7

HS=2.7m

NS=12

HS=3.6 m

NS=8

Descriptions

0.55

1

0.5

0.75

Reltn. Coeff

Storey number storey height

FAB—Floor of Artificial Brick; FCB—Floor of Ceramic Brick; IECC—Inner/Exterior Corridor Compartment. NS—Number of Storey; HS—Height of Storey; ECC—Exterior Corridor Compartment. FCS—Framework Casting on Site; SCS—Slabs Casting on Site; SPF—Slabs Pre-Fabricated. ICF—Independent Column Foundation; PFP—Pre-Fabricated Pile; IC—Inner Corridor. RCRF—Reinforced Concrete Raft Foundation; EC—Exterior Corridor; LGF—Local Grillage Foundation. CSM—Ceramic Surface Mounting; CAL—Classroom and Laboratory; ICC—Inner Corridor Compartment.

A*

FCS

SPF

Estimated

……………………………………………………………………………………

FCS

Existing

LGF

FCS

SPF

Existing

SPF

Building C2

Building C3

ICF

FCS

Existing

1

Deep Foundation ICF

SPF SemiBasement

Building C1

0.9

PFP

FCS

Reltn. Coeff

Descriptions

Descriptions

Reltn. Coeff

Foundation situation

Structural characteristics

Existing

Items

ICC

IECC

CAL

File & Document Ware house

ECC

Descriptions

Architectural combination

Table 3.1 Existing engineering building attributive data and membership relation coefficients

0.9

0.8

1

0.3

0.5

Reltn. Coeff

FAB

FAB

FAB & FCB

FAB

FAB & FCB

Descriptions

0.8

1

0.7

Reltn. Coeff

Floor material

1

16200m2

9856m2

12045m2

4293m2

Descriptions

0.3

0.3

0.4

1

Reltn. Coeff

Total Floor Areas

0.9

198

244.43

223.07

294.21

Total struct. cost (RMB Yuans) Per m2

34 3 The Conceptual Investigation of “Deep Data” and “Deep Knowledge”

3.4 Fuzzy Decision in Bidding

35

g4 represents the number of storey; g5 represents the height of storey; g6 , the situation of architectural combination; g7 represents the material of floor; g8 represents the total floor areas. (2) Select the basic component element in fuzzy set as a calibration standard, assigning the membership function as 1.0, then establish fuzzy relationship by trial-and-error method shown in Table 3.1. Define the attributes of fuzzy set of sampling building as above, and then the membership function of sampling building with respect to G is sequentially assigned in Table 3.1. (3) The fuzzy subset of sampling building to G are: ˜1 = G

(

˜2 = G

0.9 g1

(

)

( +

1.0 g1

0.6 g2

)

( +

)

( +

1.0 g2

0.5 g3

)

)

( +

( +

1.0 g3

0.75 g4

)

)

( +

( +

0.5 g4

0.5 g5

)

)

( +

( +

0.3 g5

0.5 g6

)

)

( +

( +

1.0 g6

0.7 g7

)

)

( +

( +

1.0 g8

0.4 g7

)

)

) ( ) ( ) ( ) ( ) ( ) ( ) 0.8 0.8 1.0 1.0 0.8 0.3 0.6 + + + + + + g1 g2 g3 g4 g5 g6 g7 ) ( ) ( ) ( ) ( ) ( ) ( ) ( 0.5 0.5 0.55 0.8 1.0 0.3 0.8 ˜ + + + + + + G4 = g1 g2 g3 g4 g5 g6 g7 ˜3 = G

(

The fuzzy set of expected building with characteristic attribution set G is. A* = (1.0/g1 ) + (0.7/g2 ) + (0.5/g3 ) + (0.7/g4 ) + (0.9/g5 ) + (1.0/g6 ) + (0.9/g7 ). ˜ ∗ to C1 , C2 , C3 and C4 , we take the similarity (4) Define the similarity degree of A degree of A∗ and C1 first: ˜ ∗ ⊗ C1 = (1.0 A


< > < > < > < > < (0.7 0.6) (0.5 0.5) (0.7 0.75) (0.9 0.5) (1.0 0.7) (0.9 1.0)

0.9)

> < > < > < > < > < > < (0.7 0.6) (0.5 0.5) (0.7 0.75) (0.9 0.5) (1.0 0.7) (0.9 1.0)

The similarity degree ˜ ∗ , C1 ) = 1 (0.9 + (1 − 0.5)) = 0.7 A 2 Similarly ˜ ∗ , C2 ) = 0.65; A ˜ ∗ , C3 ) = 0.55; A ˜ ∗ , C4 ) = 0.75 A (5) As mentioned above, take the top three similarity degree as α1 (= 0.75) > α2 (= 0.7) > α3 (= 0.65) and the corresponding cost E1 = 198; E2 = 294.21 and E3 = 223.07 (Yuan/square floor area).

36

3 The Conceptual Investigation of “Deep Data” and “Deep Knowledge”

(6) The expected building is cost by Eq. (3.25), the [ e ∗ A = M · λ α1 E1 + α2 E2 (1 − α1 ) + α3 E3 (1 − α1 ) (1 − α2 ) ] +1/3(E1 + E2 + E3 ) (1 − α1 ) (1 − α2 ) (1 − α3 ) = 4420.8 × 1.0 × [0.75 × 198 + 0.7 × 294.21 × (1 − 0.75) +0.65 × 233.07 × (1 − 0.75) × (1 − 0.7) + 1/3 × (198 + 294.21 + 233 + 233.07) ×(1 − 0.75) × (1 − 0.7) × (1 − 0.65)] = · · · = 217.06 × 4420.8 = 959, 578.8 (Yuan)

Practical finalized cost of expected building is provided latter on by contractor as 210×4420.8=928,368 (Yuan). The error percentage is (959, 578.8 − 928, 368)/928, 368 × 100% = 3.36%(< 5%) Case 2: The quotation of a bridge based on fuzzy reasoning of attributes from data base of existing bridges (Wu, et al., 2021). The attributes of existing bridges ZSMRO, DXRB, ….. are listed in Table 3.2 with its unit price and costs for different individual parts. The fuzzy membership function of each structural component parts are respectively represented by the relational coefficients k as presented in Table 3.1 for different items of the structural characteristics. In our analysis, k is regarded as the ratio of the cost of individual structural part with respect to the “standardized” cost of the same rank structural part, since the membership relation can also be evaluated by the comparative cost of the bridge to be examined with the “standardized” one. In addition, k is manipulated for different structural component part, which will further improve the accuracy of fuzzy estimation. Table 3.2 presents the cost data of E.R.C. bridges with the amount of concrete (M3 ), same operation as the case 1 is carried out for expected bridge ERCB. In 9-th row it shows that ERCB is quoted as 1345.3, yet the real cost is 1407.7, the error rate is. (1407.7 − 1345.3)/1345.3 = 0.046 or 4.6%(< 5%). (1407.7–1345.3)/1345.3 = 0.046 or 4.6%.( Max(E VE )R , the decision of bidding or investment can be approved directly; • If Min(E VE )R ≤ Max(E) ≤ Max(E VE )R , the decision of bidding or investment can be suggested to be approved; • If Max(E) < Min(E VE )R , the decision of bidding or investment can be considered to be approved. (3) If Max(E) is located in RE domain, compare the RE domain of Figs. 4.10 and 4.11: • If Max(E) > Max(E RE )R , the project can be approved directly; • If Min(E RE )R < Max(E) ≤ Max(E RE )R , the project can be suggested to be approved; • When Min(E RE )R > Max(E RE )B , the project can be considered to be approved if Max(E) ∈ [Max(E RE )B , Min(E RE )R ];

4.7 Conclusive Remarks

57

• If Min(E RE )B ≤ Max(E) < Max(E RE )B , the project can be suggested to be rejected; • If Max(E) < Min(E RE )B , the project can be certainly to be rejected directly. (4) If Max(E) is located in SE domain, compare the SE domain of Figs. 4.10 and 4.11: • If Max(E) > Max(E SE )R , the project can be approved directly; • When Min(E SE )R > Max(E SE )B and Max(E) ∈ [Max(E SE )B , Max(E SE )R ], the project can be considered to be approved; • If Min(E SE )B ≤ Max(E) < Max(E SE )B , the project can be suggested to be rejected; • If Max(E) < Min(E SE )B , the project can be definitely suggested to be rejected. (5) If Max(E) is located in NG domain, compare the NG domain of Figs. 4.10 and 4.11: • If Max(E) > Max(E NG )B , the project can be considered to be rejected; • If Min(E NG )B ≤ Max(E) ≤ Max(E NG )B , the project can be suggested to be rejected; • If Max(E) < Min(E NG )B , the project should be definitely suggested to be rejected.

4.7 Conclusive Remarks Project risks in the engineering practice are serious, especially for those projects in a new market. As the risk emerged, the corresponding countermeasures should be taken; thus, there will be a complicated relationship between risk and its countermeasure (knowledge) with a ‘Game Relation’ between each other, such as “this grows with that vanishes” and “this vanishes with that grows”. The composition of risk of a project is so complicated that it comprised of hierarchical and subordination relations. Consequently, the corresponding countermeasures (knowledge) would be engaged in this relation; then a sophisticated non-linear network ‘RiskKnowledge Framework (RKF)’ is formulated with “one-to-one”, “one-to-multiple” and “multiple-to-multiple” relations between individual risk and its countermeasures. This situation is more challengeable for project risk management, since it is impossible to realize an overall comprehensive manoeuvring of those severe and hostile project risk system just by empirical and heuristic knowledge of the project decision maker. It seems appropriate and reasonable for realizing quantitative project risk management based on a reasonable fuzzy mathematical modelling and by means of computer science versus AI knowledge engineering technology. Human being can’t stand on routine work, and computer can’t stand on logic confusing. Though people are unable to deal with the complexity just by their subjective judgment, however, they could establish mathematical model for dealing and solving the sophisticated interactive coupling relationship between risks and

58

4 Fuzzy Quantitative Risk Management

knowledge by means of computer. The mathematical model will work well if the completeness of risk set and knowledge set are comparatively sufficient. The next problem is how computer helps people to evaluate risk degree during decision making? How the uncertain evaluation of countermeasure (knowledge) could work in constraining the risk? The concept of fuzzy sets and fuzzy membership vector are introduced to evaluate quantitatively the “effectiveness of constraint” for the particular risk by its corresponding countermeasures (knowledge). Moreover, through fuzzy inference, the level of each particular risk in the RKF network is assessed quantitatively. Thus, the theory and practice presented in this chapter are devoted to solve the risk management under complex project environment, which is developed with fuzzy intelligent inference. By applying knowledge engineering methodology, the subjective interferes of decision maker to the final decision solution can be avoided. The complexity of project risk could properly treated by regular and reasonable framework of solution procedure. This is a valuable experiment for promoting the progress in decision science by fuzzy set mathematics and for upgrading the risk management from the scenario of qualitative to quantitative. The method, presented in this chapter is also a kicking off test of practicing quantitative management theory. There are more theoretical and practical works of quantitative management theory need to be investigated in the future; such as the RKF network optimization problem, the system error estimation of solution by means of fuzzy membership vector, etc. A variety of statements in fuzzy risk quantitative management are also discussed in different papers and monographs all over the world as a hot topic in academic research, it is also worthwhile to recommend to our readers for further investigation (Alves et al., 2021; Lin, 2014; Lin et al., 2009; McNeil, 2005; Mudashuru et al., 2021; Sharma et al., 2011; Timothy, 1995; Xu & Lin, 2016).

References Alves, J. L., Ferreira, E. A., & de Nadae, J. (2021). Crises and risks in engineering project management: A review. Brazilian Journal of Operations & Production Management, 18(14). Lin, S. (1998). Fuzzy-AI model. In Proceedings of 2nd International Conference on Artificial Intelligence for Engineering, May 1998, Wuhan, China, pp. 56–72 Lin, S. P. (2014). The principle and methods of quantitative management—Fuzzy-AI model in managerial science. Taiwan Project Management Network, Taiwan (in Chinese). Lin, S., Zheng, H., et al. (2012a). The optimization of metro maintenance based on FMEA-fuzzy model (I). Journal of Optimization in Infrastructure Management, 24(2), 22–28 (in Chinese). Lin, S., Zheng, H., et al. (2012b). The optimization of metro maintenance based on FMEA-fuzzy model (II). Journal of Optimization in Infrastructure Management, 24(3) (in Chinese). Lin, S. P., Zheng, H., Hu, H., & Yan, J. (2009) Fuzzy-AI modelling for optimization of longterm metro vehicle repair. In Y. X. Chen, H. P. Deng, D. G. Zhang, & Y. Y. Xiao (Eds.), Fuzzy Systems and Knowledge Discovery, 2009. FSKD ‘09. Sixth International Conference on Volume: 6. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, pp. 459–463. McNeil, A. J., Frey, D., & Embrechts, P. (2005). Quantitative risk management: Concepts, techniques, and tools. Princeton Series in Finance.

References

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Mudashuru, R. B., Sabtu, N., & Abustan, J. (2021). Quantitative and semi-quantitative methods in flood hazard/susceptibility mapping: A review. Arabian Journal of Geosciences, 14(11). Lin, S. (2008). “Fuzzy-AI model” for managerial science. Plenary Session Speech and Proceedings of 4th PMI Research Conference, 13–16 July, 2008, Warsaw, Poland. Lin, S., Zhen, H., Yan, J., Hu, H. (2010). RCM-Fuzzy model and its applications to maintenance with cost/serviceability optimization. In Proceedings of IEEE, The PACIIA Conference, 4–6 December 2010, Wuhan, China Sharma, A. G., Yadava, S., & Deshmukh, S. G. (2011). A literature review and future perspectives on maintenance optimization. Journal of Quality in Maintenance Engineering, 17(1), 5–25. Timothy, J. R. (1995). Fuzzy logic with engineering applications. McGraw Hills. Xu, F., & Lin, S. P. (2016). Theoretical framework of Fuzzy-AI model in quantitative project management. Journal of Intelligent and Fuzzy Systems, 30(1), 509–521. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353. Zhu, W., Lin, S. P., & Hu, H. (2014). Optimization of overseas project risk management based on “Fuzzy-GA” model. Journal of Optimization in Infrastructure Management, 26(2), 2–9 (in Chinese).

Chapter 5

Quantitative Risk Decision of Overseas Projects

5.1 Project Risks in Overseas Engineering Market Project risk is come from uncertainties of the project environment; if there is every thing is certain, there will be no risk at all. Unfortunately, there are more uncertainties in the overseas market; therefore, there will be more risky for the project under overseas market. This chapter entitled in “Quantitative Risk Decision of Overseas Projects” is devoted to fuzzy quantitative studies in project risks with its philosophical background and its decision making essentials, such as the decision traps in overseas projects (Shaopei & Wengyan, 2011). For management is the highest intelligence of human being, which is represented by the capacity of intuitively mass fuzzy information processing. Using computer to simulate human intelligence with fuzzy approach forms the “Fuzzy-AI modeling”. Based on this model an efficient tool by means of computer AI technology is introduced, which can simulate human intelligence to perform the digitized decision inference or quantitative information of management. Practical examples with a broad area of engineering application in the overseas market are illustrated in this chapter for reference, which is extremely important in project risk management problems in overseas market, especially for those severe and complicated mega projects. The emphasis in this chapter is focused on the overseas project risk evaluation and control by knowledge engineering. Based on the analyses different risks of overseas project, the RKF framework (Risk-Knowledge-Framework) is established for applying fuzzy inference to determine the risk degree of the project. The well developed software KB-FDSS as mentioned in above chapters can make quantitative evaluation of the project risk as the argument of investment decision making for the engineering projects. It is doubtless that the assessment of project risk in the engineering practice is so important, for without correct evaluation of the project risk, the investment decision making could be entirely wrong, which may be the critical reason of directly causing project failure. Therefore, to discuss the fuzzy mathematical modeling of risk © Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 S. Lin and G. Zhao, Fuzzy Quantitative Management, Fuzzy Management Methods, https://doi.org/10.1007/978-981-10-7688-6_5

61

62

5 Quantitative Risk Decision of Overseas Projects

evaluation as well as the theorem of fuzzy inference, are necessary for approaching the practical risk assessment of the project. In applying fuzzy comprehensive risk assessment, the method presented in this chapter is just to provide fuzzy quantitative information to the project decision maker in reference and assistance for his (her) project decision. Even though this information is in ambiguity, yet it is concluded by comprehensive analysis of various factors existing in the complicated project environment; moreover, it also provide quantitatively the global and individual risks of the project, which could be clarifying the direction of how to dealing with different risks of the project for the project decision makers.

5.2 Modeling of the Problem 5.2.1 The Contents and Characteristics of Project Investment Risk (1) The implementation of project is associated with risk. Since uncertainties are deemed to be existed with project environments, which will be the source of risks. However, whether the project is success of failure will depend on the risk control, especially for those overseas projects, the project environment is full of uncertainty, it will increasing the project risks. The fundamental task of the project decision maker is how to analyze, control, mitigating and avoiding the project risks for guaranteeing the project is proceeding under limited and controllable risks, avoiding the losses of life and properties which will directly causing project failure. It is mentioned that not every project could control its risks within the controllable scale, under such circumstance, the project decision maker(s) will have no choice but abandon the project. (2) As the risk is existed in every project, one has to take corresponding measures (knowledge) against it; the inter-relationship of risk and knowledge has the feature of implicit complexity and formed a hierarchical non-linear network. Under the overseas project investment case, the challenges of severe risks cannot be conquered by just individual subjective judgment and usually will loss of its control; therefore, it is necessary to investigate the laws of initiation of project risk as well as its countermeasures of its mitigation and avoiding. (3) There still exist an uncertain problem of how much constrain can be provided by the countermeasures (knowledge) to a specific risk? For the feature of events are volatile and ambiguous, its solution should be solved by in-deterministic mathematics, so we introduce the concept of fuzzy mathematics and using fuzzy membership function for describing the effectiveness of the constraint to obtain the global risk degree and second degree risks through fuzzy inference. Thus the fuzzy risk quantitative assessment of the complicated problem can be provided

5.2 Modeling of the Problem

63

for the arguments of fuzzy quantitative decision making to be the reference of the project decision makers.

5.2.2 The Indicator System of Economic Risks in Project Investment In the analysis of economic risks of project investment, the establishment of reasonable assessment system is beneficial to the systematic, comprehensive and simplified risk assessment; it also clarifies the position and inter-relationship of different indicators in the economic risk assessment processes; so as to select the indicator correctly according to different objectives and scopes in overseas project investment. For the purpose of rectification and systemization of economic risk analysis in overseas project investment, Fig. 5.1 shows the evaluation indicator system of overseas project investment. In Fig. 5.1, each risk of investment and financing, risk of debt, risk of inflation, risk of working period, risk of revenue and operation and risk of interest rate are either inter-independent and also inter-related. For an example, the independence of risk indicator of revenue and operation are expressed by the deficiency of income revenue may cause the increasing of unit operation cost; the inter-relation of risk indicator of revenue and operation are expressed by the increasing of operation cost due to inflation. V1 --- Risk of investment and financing V2 --- Risk of debt

V3 --- Risk of inflation Economic Risk Indicators

V4 --- Risk of working period V5 --- Risk of revenue and operation

V6 --- Risk of interest rate Fig. 5.1 Evaluation of risk indicator system of project investment:

64

5 Quantitative Risk Decision of Overseas Projects

5.3 Soft Strength and Human Error Risk in Overseas Projects In this section, human factor error induced risk and human recognition risk are discussed, which are another scenario of traditional project risk analysis and then possesses broad space for future studies. These theorems are related to the deep reasoning of why that overseas project risk will be amplified to certain enterprise? And what will influence to build “Soft Strength” and to increase market competitiveness of the enterprises?

5.3.1 Risk Control and “Soft Strength” of Enterprises The project environment is full of uncertainties and which is the source of the risk. There is nothing certain but uncertainties are the only certainty during project implementation. Especially, for the overseas projects, people don’t know by each other, how can an enterprise be impressed by its counter-part(s) is really a critical issue; among them, the “soft strength” of the enterprise will play an important role in their interaction. Since “soft strength” of an enterprise will be the only expression to its counterpart(s); and based on which their counterpart(s) will deal with them in different attitudes. If the “soft strength” of an enterprise is powerful enough, their counterpart(s) will “well treated” them correspondingly; however, if the “soft strength” of an enterprise is weak, the unfavorable and serious treatment by their counterpart(s) will reveal. The project decision maker will make solution for every event during project implementation according to the project environment. Whether his/her decision is correct or not will heavily influence to the appropriateness of the decision making. It is inevitably the decision will be influenced by the judgment of the decision maker, which is a crucial risk of “human factor” error caused by the personality, qualification and constraints of managerial system of the enterprise as well. Indeed, this is related to category of building “soft strength” of the enterprise. Besides, one of the critical issues for the enterprise in “soft strength” in the international market is how to establish its “positive image” explicitly from the outside. Among them, the credibility of the enterprise is as crucial as the foundation of its market survival. Unfortunately, in recent years, a minority of enterprises have hurt the reputation of majority of enterprises by its irregularity of market conduct, thus it seems that the establishment of certain constraints for revealing positive image in the overseas market is critically necessary. This is another problem related to the category of building “soft strength” of the enterprise. After all, no matter this “human factor” error is caused by personal qualification of the decision maker, or caused by less maturity of the managerial system of the enterprise (the maturity of the enterprise can be regarded as the group human factor influence), thus, out of control in overseas project risk management will occur,

5.3 Soft Strength and Human Error Risk in Overseas Projects

65

risk induced economic losses of the project will follow. Such frequently encountered phenomena also reflect the weakness of the “soft strength” of the enterprise in organizational behavior.

5.3.2 Human Factor Induced Decision Error in Overseas Project Risk Management Due to “human factor” the decision maker may always under the risk of “decision trap” of taking wrong decision. Since the project environment is changeable all the time and in all the different places, therefore, to study the law of its variation due to different understandings of culture recognition is also a problem of critical importance. Below, we list 15 practical engineering examples (Chao & Shouqiang, 2007) conducted by Chinese overseas enterprises in different countries with different situations and different consequences: (1)

Indonesia BOOT (Build-Own-Operation-Transfer) mode with 150 MW electric station project, which became a model of infrastructure construction and successful sample for foreign investment of the country; (2) Bangladesh EPC (Engineering-Procurement-Construction) mode bridge project, which was completed on time with highly appraisal by the Bangladesh Government; (3) Sudan bridge project with general contractor mode, which was well built and submitted for commissioning in advance; (4) Indian power engineering project with EPC mode, which was completed on time and obtained highly appraisal by the local Government; (5) Afghanistan highway project with EPC mode, which was completed on time and obtained the offers of many subsequent projects; (6) Bangladesh mine engineering project with EPC mode, which was completed and transferred on time; (7) Venezuela western railway re-construction project with EPC mode, which was completed and transferred on time and thus keeping with client with long-term cooperative relationship; (8) Iran subway project with EPC for electro-machinery, in which the schedule was delayed and the subsequent project opportunity was achieved through the social benefits of the project itself; (9) Sudan S building project with EPC mode, unfortunately, the cost of the engineering project was seriously exceeded to the budget; (10) An African upgrading of highway project with the construction general contractor mode, which is unfortunately that the schedule was delayed due to the failure of project management;

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(11) Another African power station extension project with general contractor mode, in which the schedule was seriously delayed due to the failure of project management; (12) Thailand offshore platform manufacturing project with construction general contractor mode, which finally due to failure in completion of contract quantity and the client terminated the contract; (13) Nepal power station project with EPC mode, which was failed due to blindly tendering and insufficient investigation cause fail to communicate with client; (14) Saudi-Arabian light railway project with EPC + Operation + Maintenance mode. The consequence of which was severe deficiency though the project was completed smoothly; (15) Poland highway engineering project with EPC mode, which was serious deficiency due to low quotation in tendering during bidding competition without detailed risk consideration, causing payment delay and a huge prepayment by the contractor. The project was forced to be terminated and leading to claim disputes. We start from the theory of managerial psychology to examine the above both successful and failure projects, and found the subjective factors falling into decision traps (Wenyan & Shaopei, 2012) are the main reason of failure in those failed projects. By combining analysis of “human factor” and “decision trap”, the fuzzy inference model of quantitative assessment of overseas project risk can be achieved for the risk management decision supports.

5.3.3 Factors and Traps of Wrong Decision How the subjective factors falling into decision traps? We may discuss the factors and traps of wrong decision; the failure of overseas projects is frequently caused by insufficient preparation work or wrong decision making in different stages of the project; the problems are illustrated as followings; (1) Have adequate researches before the mergers (investment) business been well taken? What is the future market potential of new enterprises in the early period? (2) Are the project objectives clear? Are feasibility researches with full details? (3) Have adequate technical preparation is taken before contracting or investing? (4) Any other risks of stepping into decision traps by the project decision maker before tendering and investing of the project? (5) Have adequate control and management measures for technical, managerial and market integration after contracting or investing been taken? (6) Have adequate preparation and measures after contracting and investing be fully engaged in control and management? (7) Have adequate preparation after contracting or investing for fully engaged your main stream of culture to the new enterprise been taken?

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(8) Have enough preparation works been arranged for market development and types of sales after contracting or investing? Whether those decision making mistake factors mentioned above will act will depend on whether the decision makers have stepped into the traps. From managerial psychology point of view, the initiation and processes of why decision maker stepped into these traps have its law of psychology.

5.3.4 Psychological Analysis of Decision Trap Once the decision maker trampled into the trap, the project will be deemed to develop along a wrong direction. By managerial psychology, the decision makers often indulge in the initial impression, they are afraid of proving their initial policy is wrong through objective facts. On the contrary, they intend to spend additional efforts for proving their correctness through finding one sided evidences. The decision makers who are in the leading position are usually fragile in psychology and are difficult to distinguish and correct the mistakes timely. In practice, we could identify the decision traps as below: (1) Trap of cognition deviation in decision making, which is with impetuous mind, radical thinking and deviated cognition, and considering A to be B; (2) Trap of loss of management control in decision making refers to a myriad of thoughts and confused thoughts, which are with no rules and lose control over management; (3) Trap of wrong direction in decision making, which points to A but talks about B with less of consciousness and more of confusion, and goes in a wrong direction; (4) Trap of inadequate investigation in decision making, which is with great determination, but unfamiliar with the situation and lack of investigation because of lazy to investigate; (5) Trap of erroneous estimate in decision making refers to empiricism, dogmatism with no interaction with outsiders and making erroneous estimates; (6) Trap of moral defection in decision making refers to swindle, dishonesty, willing to harm others and moral defects; (7) Trap of deficient study in decision making trap. Diligence and concentration bring success and excellence while laziness and random lead to failure. With no idea in head is due to lack of study; (8) Trap of irresponsibility in decision making. Make faults in projects but the state pays for the bill. Remaining in high power when change to the new post. There are still ethical issues in the cross-cultural differences. People with different cultural background may have quite different opinions on the same issue. Such as it is acceptable to bribe the custom officials in some countries, because these governments determine their wages according to whether they have the possibility to make some extra money. But in most countries regard it as unacceptable because they think that

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only if everyone becomes honest can the system become more effective. Another example says that in executing a contract, some think that the contract is just a kind of mutual indenture defining both sides’ relationships only; when executing, there could and should be some flexibility. While in most countries, people emphasize the seriousness when carry out a contract and once there appears some discrepancy, people must ask for compensation according to the contract. Those cross-cultural differences are some of the reasons that get the overseas projects into danger.

5.4 Case Studies 5.4.1 Real Estate Project Investment Risks in New York City The real estate project is located at downtown New York City, it is a reinforced concrete framework structure; the total investment is 130 Million USD. According to market analysis of this region, there is a tendency of price increasing in the residential and commercial buildings. Even though after US economic crises in 2008, the real estate price in this region still remain high level, since the downtown area of NYC is very convenient in traffic, the infrastructure in commercial and living condition are perfect, the economic status in US is stably recovered, so there is an optimistic estimation in real estate price, especially in those residential, office buildings. According to market estimation, only the hotel session of this project may cost 55.3 Million USD. For real estate investment project, the risk indicator system of project investment will subject to corresponding changes; that will be: financing risk, economic risk, legal risk, market risk, managerial risk and investment limitation risk. Using mathematical modeling to evaluate fuzzy risk of this real estate project (Shaopei, 2013), we obtain the result as shown in Fig. 5.2, which is a project with negligible risk. Its fuzzy membership “negligible” NG = 0.5132, higher than “Median” MD = 0.2969, “Rather serious” RS = 0.1335 and “Very serious” VS = 0.0562. Figure 5.3 represents the final result of fuzzy financing risk of real estate project; its result of inference also is “Negligible risk”. The fuzzy membership NG = 0.7125, which is higher than the “Median risk” MD = 0.1917, “Rather serious” RS = 0.0775 and “Very serious” VS= 0.0183. Figure 5.4 represents the result of fuzzy evaluation of economic risk of real estate project, the final result is: “The risk does not exist”. The fuzzy membership value in all risk evaluation sectors equals to zero. Figure 5.5 represents the fuzzy legal risk evaluation of real estate project in NYC; its inference result is “Negligible risk”. The fuzzy membership value “Negligible” NG = 0.775 is higher than all other fuzzy membership values MD = 0.15, RS = 0.065 and VS = 0.01.

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Inference result: Negligible risk. VS(red) 0.06215; RS (yellow) 0.1335; MD (blue) 0.2969;NG (green) 0.5132 Risk map

Risk column NG MD RS VS

Financing risk Economic risk Legal risk Market risk

Fig. 5.2 Results of fuzzy risk evaluation of real estate projects in NYC

IR1 Financing risk Inference result: Negligible risk.

Risk map

VS(red) 0.016333; RS (yellow) 0.0775; MD (blue) 0.19166; NG (green) 0.71255

Financing obtaining Financing cost Financing structure Credit risk

Fig. 5.3 Fuzzy financing risk evaluation of real estate project in NYC

Figure 5.6 represents the fuzzy market risk evaluation of real estate project in NYC; its inference result is “Median risk”. Since the MD = 0.4333 is higher than all other fuzzy membership values, such as “Negligible” NG = 0.35, “Rather serious” RS = 0.15 and that of “Very serious” VS = 0.0666. Figure 5.7 represents fuzzy management risk evaluation of real estate project in NYC; its inference result is: “Slight risk”. Since the fuzzy membership value of “Median” risk zone is MD = 0.3489; which is higher than “Negligible” zone NG = 0.3277, “Rather serious” zone RS = 0.2127 and “Very serious” zone VS = 0.1105. Figure 5.8 represents the fuzzy investment restriction risk evaluation of real estate project in NYC, its inference result is “Negligible risk”. Since the fuzzy membership value NG = 0.6625, which is higher than all fuzzy membership values of other zones: MD = 0.225, RS = 0.0875 and VS = 0.025. According to above mentioned analyses, this investment project possesses the feature of “Negligible risk”, which is quite coincidence to the conclusion “The

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IR2 Economic risk Inference result: Risk does not exist

Risk map

Risk map VS(red) 0 ; RS (yellow) 0; MD (blue) 0; NG (green) 0.

Risk of macro-economy Risk of exchange rate Risk of interest rate Risk of inflation Risk of currency exchange

Fig. 5.4 Fuzzy economic risk evaluation of real estate project in NYC

IR3 Legal risk Result of evaluation: Negligible risk

Risk map

VS (red) 0.01; RS (yellow) 0.065; MD (blue) 0.15; NG (green) 0.775.

Investment mainstay Project review Taxation system Labor law Environment risk

Fig. 5.5 Fuzzy legal risk evaluation of real estate project in NYC

project is controllable” from experts’ subjective judgment and evaluation. As for the quantitative overseas project risk, one may refer to reference (Feng et al., 2014).

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IR4 Market risk Result of evaluation: Median risk VS (red) 0.06666 RS (yellow) 0.15 MD (blue) 0.43333 NG (green) 0.35

Risk map

Market degradation Market limitation Market counteract Market competition Resident preferable

Fig. 5.6 Fuzzy market risk evaluation of real estate project in NYC

IR5 Management risk Result of evaluation: Median risk

Risk map

VS (red) 0.1105555 RS (yellow) 0.2127777 MD (blue) 0.3488888 NG (green) 0.327777

Risk of execution Professional ethics Wrong decision Technical risk Contract risk

Fig. 5.7 Fuzzy management risk evaluation of real estate project in NYC

5.4.2 High-Speed Railway Investment Risks High-speed railway system is now enter a fast development era, as the overseas project investment is far reaching to a long distance location from the host investment countries, which is an important traffic approach with massive transportation, high speed, time exactness, less pollution, safe and comfort; moreover it is still a stimulating tool for supply chain development as well as for the economic growth of related nations. However, any kind of project is associated with risk, which is a

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IR6 Investment restriction risk Risk map

Result of evaluation: Negligible risk VS (red) 0.025 RS (yellow) 0.0875 MD (blue) 0.225 NG (green) 0.6625

Market permission Condition of counterpart Taxation

Fig. 5.8 Fuzzy investment restriction risk evaluation of real estate project in NYC

real existing fact without any constrain measures of human being. As the important engineering construction of infrastructure, the high-speed railway system investment certainly will subject to various risks especially to the economic risk. Moreover, the high-speed railway construction is located in an open systematic environment; it is difficult to handling the law of economic risk changing due to the changes of construction conditions, the changes of design by client’s objectives orientation, the drawbacks of the construction plan and the emergence of uncertain risks, etc. Using the fuzzy-AI model (Shaopei, 2008), here we present the verification of effectiveness in controlling the economic risks in high-speed railway investment (Feng & Shaopei, 2014). As we establish similar Fig. 5.1 the risk indicator system of project investment for high- speed railway system, we define W as the weight set between risk of investment and financing, risk of debt, risk of inflation, risk of working period, risk of revenue and operation as well as interest rate, it reads: W = (0.15, 0.10, 0.25, 0.20, 0.15, 0.15)

(5.1)

The economic risk of high-speed railway system investment, its risk degree set B = {Very serious risk, Rather serious risk, Median risk, Negligible risk}; we need to define by the expert the fuzzy membership values of six risk indicators to the four risk degree levels rij . In order to avoid the misleading by the subjective judgment of individual expert, we define the unified simple standard of fuzzy membership vectors for different levels of economic risks as below: (1) For very serious economic risk (VS, Very Serious), the fuzzy vector is:

RVS = {0.85,

0.1,

0.05,

0}

(5.2)

(2) For rather serious economic risk (RS, Rather Serious), the fuzzy vector is:

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RRS = {0.60,

0.20,

0.15,

0.05}

(5.3)

(3) For median economic risk (MD, Median), the fuzzy vector is:

RMD = {0.05,

0.20,

0.50,

0.25}

(5.4)

(4) For negligible risk (NG, Negligible), the fuzzy vector is:

RNG = {0.05,

0.15,

0.20,

0.60}

(5.5)

From above mentioned economic risk evaluation indicators given by expert, we obtain the negligible investment and financing risk V1 , median debt risk V2 , rather serious inflation risk V3 , rather serious period risk V4 , very serious revenue and operation risk V5 and median interest rate risk V6 . Then, following matrix R composed by risk level and fuzzy vector can be formed: VS

RS

MD

NG

0.05

0.15

0.20

0.60

V1

0.05

0.20

0.50

0.25

V2

0.60

0.20

0.15

0.05

V3 (5.6)

0.60

0.20

0.15

0.05

V4

0.85

0.10

0.05

0

V5

0.05

0.20

0.50

0.25

V6

Therefore, the results of economic risk can be calculated as: ⎧ 0.05 0.15 0.20 ⎪ ⎪ ⎪ ⎪ ⎪ 0.05 0.20 0.50 ⎪ ⎪ ⎪ ⎪ ⎨ 0.60 0.20 0.15 E = [0.15, 0.10, 0.25, 0.20, 0.15, 0.15] 0.60 0.20 0.15 ⎪ ⎪ ⎪ 0.85 0.10 0.05 ⎪ ⎪ ⎪ ⎪ 0.05 0.20 0.50 ⎪ ⎪ ⎩ VS RS MD E = [0.4175,

0.1775,

0.23,

0.175]

⎫ 0.60 ⎪ ⎪ ⎪ ⎪ 0.25 ⎪ ⎪ ⎪ ⎪ 0.05 ⎪ ⎬ 0.05 ⎪ ⎪ 0.00 ⎪ ⎪ ⎪ ⎪ 0.25 ⎪ ⎪ ⎪ ⎭ NG (5.7)

From above fuzzy inference and the principle of maximum membership function, the maximum component of E is 0.4175 in the VS region, so the economic risk of this

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high-speed railway investment project is “Very Serious”, specific countermeasures should be taken for carefully analyzing the economic risks to identify the main influence factors of the problem and taking correspondent measures against it. As for the maintenance with cost/serviceability optimization of traffic wagon, one may refer to reference (Shaopei et al., 2010). It is noticed that the original (first) stage investment of the high-speed railway construction is huge, which can be the decisive action for the success or failure to the project; however, the operation risk is just due to the operation income cannot reach the assigned income schedule, which causes the failure of return the input of first stage investment. If the economic risk cannot be mitigated or deleted, to abandon the project may be a practical and reasonable choice.

5.5 Conclusive Remarks The economic risk analysis becomes the important consideration factor for the project investment decision, especially for those overseas investment projects, where more severe economic risks exists due to more uncertain market and environmental conditions. There are two different parts of risk analysis in overseas projects: The building of risk evaluation indicator system (risk knowledge framework RKF) and fuzzy modeling analysis method and its computer software “Knowledge Bases Fuzzy Decision Supporting System (KB-FDSS)”. The former one could provide fuzzy quantitative evaluation for any specific risks; and the latter one could provide global and individual risk evaluations for the project. Two case studies using KB-FDSS software presented in this chapter including both for the overseas real estate projects in NYC and the economic risk assessment of high-speed railway construction project, have provided favorable results coincident to ideal reality, which once again prove the effectiveness and accommodation of the methodologies in this chapter to the risk management for overseas projects.

References Chao, Z., & Shouqiang, W. (2007). Case studies of China overseas contractor engineering projects. Journal of China Construction Engineering Press. (in Chinese). Feng, X., & Shaopei, L. (2014). Economic risk analysis of high-speed railway investment based on “Fuzzy-AI Model”. Journal of Project Management Techniques 1. (in Chinese) Feng. X., & Shaopei, L. et al. (2014). Quantitative solution of overseas project risk management by knowledge engineering. In: ASCE proceedings of international conference on sustainable development (IC-SDCI 2014), Shanghai, China. Shaopei, L. (2008). Fuzzy-AI Model for managerial science. In: Plenary session speech and proceedings of 4th PMI research conference, Warsaw, Poland.

References

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Shaopei, L. (2013). Quantitative risk decision of overseas projects (I) and (II). Journal of optimization in infrastructural management 3(4). (in Chinese). Shaopei, L., Hong, Z., Jun, Y., & Hao, H. (2010). RCM-fuzzy model and its applications to maintenance with cost/serviceability optimization. In: Proceedings of IEEE, the PACIIA conference, Wuhan, China. Shaopei, L., & Wengyan, Z. (2011). The decision traps of overseas projects and its fuzzy quantitative management of risks. ICE Journal of Civil Engineering Innovation, 5(1), 29–40. Wenyan, Z., & Shaopei, L. (2012). The factor analyses of risk decision traps for overseas projects. Journal of Optimization in Infrastructure Management, 24(1), 13 (in Chinese).

Chapter 6

System Dynamics Modeling and Applied to International PPP Project Risk Evaluation

6.1 Development Characteristics of System Dynamics The system dynamics modeling was appeared in 40s last century by Professor J.W. Forrester of Massachusetts Institute of Technology (MIT) (Forrester, 1961, 1968, 1985, 1969), it has been developing since 70s of last century and applied in many fields of social, technical and engineering events, such as land planning, regional development, environmental maneuvering and enterprise strategic studies. Actually, system dynamics as a new methodology and important modeling of system engineering discipline has been entering in different areas in our real life. The objects of system dynamics are in social (economic) system, which is characterized in: (1) There exist decision part in social system, it involves collection of information and information processing under certain strategies and policies; then the final decision is the result of multi-comparison, re-selection and optimization; (2) Social system has the feather of self-discipline; it is due to the fact that there is internal inherent feedback system in social system; (3) The non-linearity of the social system: It reflects non-linear relationship between the reason and result, such as the reason and result are separately in different time and location; serendipity of events, as well as intuitiveness of events. The characteristics of system dynamics model can be concluded as (Richardson, 1981): (1) The system dynamics model possesses multi-variable nature; it is the result of dynamic nature of the system and complexity of the system; (2) The combination of qualitative and quantitative analyses; it is composed by structural model and digital model (DYNAMO Equation); (3) The system dynamics model is using simulation experiment as its fundamental mean and using computer as its tool, it is essentially a computer simulation method and the physical laboratory of real system; © Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 S. Lin and G. Zhao, Fuzzy Quantitative Management, Fuzzy Management Methods, https://doi.org/10.1007/978-981-10-7688-6_6

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Identifying the problem

Simulation analysis

Confirm the system

Establish quantitative analysis model

(DYNAMO Function) Comparison and evaluation

Factor analysis and Causing relation analysis

Establish Structural model

(Flow process)

Policy and strategy analyses

Fig. 6.1 The framework flow of how system dynamics model

(4) Therefore, the system dynamics model can be used in high order mode, multinetwork, non-linear and complicated network problems. The framework flow of how system dynamics model works is shown in Fig. 6.1: Following to reference (Lin, 2019a, 2019b) this chapter is devoted to its system analyses and system dynamics solution for the purpose of providing concrete sample to practical workers in their analyses. Focus on the special topics in the risks of overseas PPP projects, seriously investigate such as soft risky worthwhile value, financial carrying capacity, performance appraisal, risk-sharing, re-negotiation, and life-cycle project management multi-related factors affected to overseas PPP project risk management, etc. For the purpose of taking the dynamic inter-relationship of these factors, the system dynamics modeling framework is thus established; furthermore, the fuzzy analytic hierarchy process (FAHP) methodology is used for analysis and evaluation to the risks of overseas PPP project.

6.2 System Analysis of Overseas PPP Project Risks 6.2.1 The Concept and Characteristics of System Science System science synthesize the treatment of objective world into a systematic and philosophical comprehension; so-called system analysis is a methodology and tool for analyses the objective world by means of system philosophical thinking; and so-called system engineering is the procedures of applying system thinking, through

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79

system analyses, to handle the practical problems. In conclusion, system methodology makes it possible for human being to use the concept of system philosophy and system analyses for raising their capabilities against the challenges during the processing of solving complicated objective events.

6.2.2 The Capacity Expected by System Methodology The core knowledge structure of system methodology is for the founding of system thinking and management capacity of the managerial persons; for answering to the solving problems of these questions, 5W3H is introduced: that means what to do? Why to do? Who, When, and where to do? Then, how to do? how long does it take? and how much does it costs? So as to build the system solving framework for the solution of huge complicated projects.

6.2.3 Elements of System Component The system is composed by the components, attributes and relationship. The components possess the function of operation, including input, procedure and output. Attributes are the elements of components of the system as well as the visible characters, which determine the characteristics of the system. Relationship represents the relation between components and attributes.

6.2.4 The System Risks of Overseas PPP Projects (i)

The final system behavior of overseas PPP project is influenced and determined by the interactions of various factor attributes and the system functional behaviors. Such as the final risks of an overseas PPP project are formed by the interactions of various influence factors (sub-system). (ii) The various system factors will influence to the system behavior, such as the risks of overseas PPP project are formed by the comprehensive behavior of various sub-systems. i.e., by the inter-relative relation of soft risky worthwhile value, financial carrying capacity, performance appraisal, risk-sharing, re-negotiation, and life-cycle project management multi-related factors, influenced to the risk management of overseas PPP projects. (iii) In the big system analysis, the man-man relationship is far from complexity of a man–machine system. For example, the big system of risk control of an overseas PPP project is far from complexity than an electric supply system of a local area.

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(iv) Owing to the dynamic interaction, the complexity of a multi-factorial big system will be heavily increased. Such as in the risk control of overseas PPP projects in volatility, uncertainty, complexity and ambiguity (VUCA) era, will related to a big system simultaneously concerning to politics, economic, social factors etc., which will greatly increase the complexity degree of the problem. (v) Innovative system thinking and innovative techniques are the foundation of analyzing, design and solving large scale complicated problems; which is also right the approach of system solution of problem by our recognition system. In order to realize risk control of overseas PPP project, there must highly innovation in modeling of the problem as well as in the implementation of the problem solution.

6.2.5 Case Study of System Dynamics Modeling of Project Financing Risk The case study of system dynamics modeling in project financing risk represents the modeling and solution innovation of this complex problem: As shown in Fig. 6.2, the whole system dynamics modeling of project financing risks is composed by five interactive major risk factors: financing cost risk; credit risk; return risk; operation risk and capital utilization risk. Afterword, the financing cost risk is related to the changes of global economic environment, interest rate, inflation, credit level, debt magnitude, loan interest and exchange rate. Moreover, the subfactor debt magnitude will influence to return risk and sub-factor loan interest will influence to return risk and then project profitability even operation risk. At the same time, sub-factor credit level will affect to credit risk and then operation risk; thus, the system behavior of project financing risk can be modeled by system dynamics model in its system behavior.

6.2.6 The Implementation of Risk Management Overseas of PPP Project It is needed to organize the planning, investigation, design and using the scientific modeling techniques; that is the universal methodology applicable to all the system problems in the objective world. The objects of which cover all the artificial and natural system; its contents involve organization and accommodating to various factors for the purpose of total function optimization of the system and minimizing the total system risks.

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81

Fig. 6.2 System dynamics modeling of project financing risks

6.2.7 System Analysis and Action of Risk for Overseas PPP Projects Figure 6.2 shows the logic diagram of system analysis and implementation action of overseas PPP project. As shown in the Fig. 6.2, through system analysis and system. science, the objectives of risk management in overseas PPP project are identified; through system dynamics modeling, the logic of overseas PPP project risk management factors can also be defined, which can make it possible the system decision making in different period of the project and take corresponding effective actions. Based on which, the system engineering of overseas PPP project in each period can be fairly accomplished.

6.3 Risk Control and System Dynamics Model of Overseas PPP Project 6.3.1 The Advantages of System Dynamics Model The applying system dynamics model to the analysis of project investment risk in overseas PPP project possesses following advantages:

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(i)

Investment risk of overseas PPP project is a complicated issue, which possesses severe implicit and uncertainty feathers. Using system dynamics model is proper for those non-linear, continuous system with high-order and multiple causal system (ii) The risk factors influenced to overseas PPP investment project are rather complicated. One who are setting system dynamics model, should maneuvering only those main attributes which influence to control factor behavior, and neglecting those secondary attributes. Then define the model boundary according to the problem to be investigated. (iii) The risks of overseas PPP project will be existed in the whole life-cycle during project implementation; the risk factors between different periods possess time discretization, yet the system dynamics model can be effectively reflected the dynamic variation of the factor.

6.3.2 The Establishment of System Dynamics Model (i) The boundary setting of investment risk in overseas PPP project During establishment of system dynamics model, the first important issue is to define the system boundaries. To define clearly the specific concerned problems and the neglected factors according to the problem to be investigated. (ii) Establishment of system dynamics model for the investment risk management of overseas PPP project, according to the structural analysis of decision system and the investment risk management of overseas PPP project, the system framework of flow chart by means of system dynamics, then using solving model and corresponding software for its solution.

6.3.3 Soft Risk Analysis of Overseas PPP Project Soft risk analysis of overseas PPP project (as same as the rigid risk analysis) involves as following contents: (i) Humanity recognition risk HR1 and its sub-risks HR11 Religious prohibition factor. HR12 Public and media attitude. HR13 Working attitude of workers from different nationality. HR14 Government service attitude and working efficiency. HR15 Organizational and procedural Coordination between public and private sides.

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83

(ii) Worthwhile value risk VR2 and its sub-risks VR21 Risk of less objectives and precise top design of government economic development. VR22 Risk of determination of basic evaluation indicators in worthwhile value. VR23 The conformity degree of life-cycle period with the basic evaluation indicator. VR24 Degree of preciseness of parameters in quantitative model of worthwhile value. VR25 The judgment standard of life-cycle project cost with productivity. VR26 Quantitative comparison of material quantity (or its applicability). VR27 Risk identification and distribution indicator. VR28 Organizational function changing, optimization of service, legal commitment of contract, administrative monitoring and capacity of implementation of government. VR29 Evaluation of financing feasibility. (iii) Financial carrying capacity risk FR3 and its sub-risks FR31 The responsibility completeness of financial outlet (stock equity, operational subsidy, risk exposure, supporting investment). FR32 Completeness of information list in financial sustaining capacity. FR33 Balance degree of PPP project area. FR34 Completeness of organization, legislation, procedure and monitoring system of financial outlet. FR35 Completeness of planning of financial outlet (stoke equity investment + operation subsidy + risk exposure + supporting investment). FR36 Completeness of planning in stoke equity investment outlet (project capital x percentage ratio of government stoke equity). FR37 Completeness of planning in operation subsidy outlet. FR38 Completeness of planning in risk undertake outlet (transferable undertake risk cost + undertake risk cost + un-transferable undertake risk cost). FR39 Completeness of planning in supporting investment outlet responsibility (total cost of government other investment minus outlet of social capital expenses). (iv) Performance evaluation risk PeR4 and its sub-risks PeR41 Less preciseness of top design in government development ideas. PeR42 Description of project output and performance standard. PeR43 Legislation and creditable government construction. PeR44 Integrity of PPP supporting legislation. PeR45 Long-term social capital investment and social responsibility and creditability. PeR46 Participation rate of social capital. PeR47 Completeness of project normative operation and its monitor system.

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(v) Risk sharing risk RsR5 and its sub-risks RsR51 Insufficiency of estimation in financial risks responsible by the government (interest exchange rate, inflation, etc.). RsR52 Force majeure unexpected by each participants of overseas PPP project. RsR53 Credit risks caused by unable to fulfill contract and responsibilities by the participants (Government and social private side) of overseas PPP projects. RsR54 Risks of delay by the PPP project contractor, project company or government behavior which caused construction completion date delay. RsR55 Construction risks during the operation of PPP project contractor (technical risk, resource risk, rule material supply risk, operation and management risks, etc.) RsR56 Market risks, including price risk, competition risk, demand risk, etc. RsR57 Difficulties of risk sharing negotiation caused by emphasizing the selfbenefit concerned of each project main participants. (vi) Life-cycle PPP project management (1st class) risk LcR6 and its sub-risks LcR61 unclear responsibility of PPP project participants during different stages of PPP project implementation. LcR62 Creditability of information provided by the government. LcR63 Risks from the exactness of design documents during construction stage, feasibility of technical alternatives, accommodation of related engineering specification requirements, accommodation of materials and instructional manufacturing standards as well as accommodation of HSE management. LcR64 Insistency of government support and social capital investment during the operation stage of PPP project. LcR65 Proper transfer of contract structure (agreement between shareholders, honor to agreement contract, financing contract, insurance contract, etc.) during operation of PPP project. LcR66 Dispute and termination of contracts during operation stage of PPP project.

6.4 Solution of Soft System Dynamics Model for Overseas PPP Projects It is doubtlessly to emphasize that for the solution of such complicated, environmental changeable, uncertain, overseas PPP project big fuzzy system, it is impossible to solve without the system technologies. Applying dialectic opposite-unification point of view, specifying the merit side and disadvantage side factors of controlling overseas PPP project, establish the model through sufficiently use the merit factors and restrict the disadvantaged one for approaching the dynamic balancing; then establish the system model through comprehensive observation and analyses for finding out the internal law of changes. Thus, we may determine the system strategies in each stage of life-cycle duration of the project, which are the basis of system decision making in each stage as well as the arguments of the actions in each stage.

6.4 Solution of Soft System Dynamics Model for Overseas PPP Projects

85

Fig. 6.3 System analysis and implementation action of overseas PPP project

The uncertain fuzzy big system of overseas PPP project risk management can be simulated by a system dynamic model with simplified hierarchical framework as shown in Fig. 6.3; thus, we can solve this system dynamic model by means of fuzzy analytic hierarchy process (FHAP). The problem solving can be further simplified if one could neglect some factors or attributes with smaller weights.

6.4.1 Six Kinds of Soft Risks in Overseas PPP Project There are six kinds of soft risks in overseas PPP project, and there are also 5–9 sub-risks (attributes) in each soft risk, after qualitative recognize the risk as well as its sub-risks, one needs further determine the equation of each variables and the weight value of each element, quantitatively describe the inter-relationship of these elements. Then it will be able to simulate its system motion and explore the law of dynamic motion of each element in the risk management of overseas PPP project; also will be able to predict the degree of future tendency of changes of related variables, so as to realize multi-position and multi-angle risk prediction and coping. As for the uncertain fuzzy big dynamic risk management of overseas PPP project, the solving strategy is much complicated.

6.4.2 Solution of FAHP Method After building the framework of system dynamics model, using fuzzy Analytic hierarchy process (FAHP) method to solve the problem. Since FAHP is possible to solve such a complicated fuzzy uncertain problems in engineering and management fields with highly uncertain features, which possesses the capacities of effective evaluation,

86

6 System Dynamics Modeling and Applied to International PPP Project …

decision making and the potentialities of prediction and solving strategy. Therefore, it is the effective approach of solving uncertain fuzzy big system in real world. In conclusion, FAHP possesses following advantages: (1) It comprehensively integrates qualitative and quantitative methods of fuzzy inference and fuzzy decision making; (2) It facilitates the complicated system attributes divide into explicit system of hierarchical network for easy treatment; (3) It executes fuzzy inference by means of human thinking philosophy as “decomposep-evaluate-synthesize”; (4) It fully utilizes the experience and knowledge of human being and decides the weight distribution through “pair comparison” for establishing inter-relationship between factors in each layer; Applying fuzzy inference and decision making techniques through FAHP, it essentially realizes the combination of artificial intelligence and software design concept; and it is right the way of simulating the processes of human thinking and decision making.

6.4.3 Building Risk Factor Framework in FAHP System Dynamics Model Solution We define the risk management of environmental changeable overseas PPP project an uncertain fuzzy big system, its risk factors is the elements for composition of risk factor framework. Figure 6.4 shows the simplified FAHP factor framework, where: A1 Objective event to be evaluated; B1 , B2 , B3 related risk actors with A1 ; C1 , C2 , . . . , C6 Sub-factor (sub-risk) crelated to B; Such as factors C1 , C2 , C3 are related to B1 ; C2 , C3 , C4 are related to B2 , etc. The first important task for objective system modeling is to build a factor framework by means of experience and knowledge of human being; then to evaluate each factor (or sub-factor) from bottom to up layer through fuzzy mathematical inference. Following we will discuss the weight value of factors in each layer determined by the Eigen-value of “Pair Comparison” matrix.

6.4 Solution of Soft System Dynamics Model for Overseas PPP Projects

87

6.4.4 Weight Order of Sub-Risk (Attributes) Determined by Eigen-Value Solution In order to accomplish an AHP process, one needs to determine the weight order of its sub-risk attributes. If the weight distribution series between the attributes are known, then the weight of each attribute can be determined. Suppose w1 , w2 , . . . , wn represent the weight of attribute 1, 2, …, n, then all the weights of the system and its relative weight rate aij = wi /wj (i, j = 1, 2, . . . , n) are known. Then the “Pair Comparison” matrix A can be defined as: ⎤ w1 /w1 w1 /w2 . . . . . . w1 /wn ⎥ ⎢ w2 /w1 w2 /w2 . . . . . . w2 /wn ⎥ A=⎢ ⎣ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ⎦ = (aij )n×n wn /w1 wn /w2 . . . . . . wn /wn ⎡

(6.1)

where w1 , w2 , . . . , wn represent the weights of attributes 1, 2, …, n, suppose it have already known, then all the aij = wi /wj (i, j = 1, 2, . . . , n) are known. Then we may define the elements of “Pair Comparison” matrix A as: aij =

aij =

1 1 wi = = wj wi /wj aji

(6.2)

aij = aji = 1

(6.3)

wi /wk aik wi = = = aik akj (i, j, k = 1, 2, . . . . . . , n) wj wj /wk ajk

(6.4)

Multiplying weight vector w = (w1 , w2 , . . . wn )T with A, then we have: A·W ⎡

⎤⎡ ⎤ w1 w1 /w1 amp; w1 /w2 amp; . . . . . . w1 /wn = ⎣ . . . . . . amp; . . . . . . amp; . . . . . . . . . . . . ⎦⎣ . . . . . . ⎦ wn wn /w1 amp; wn /w2 amp; . . . . . . wn /wn ⎡ ⎤ ⎡ ⎤ nw1 w1 = ⎣ . . . . . . ⎦ = n⎣ . . . . . . ⎦ = n · W nwn wn or(A − n)W = 0

(6.5)

(6.6)

Since W = 0.then from(6.6) we have: (A − n)I = 0

(6.7)

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6 System Dynamics Modeling and Applied to International PPP Project …

Equations (6.6) and (6.7) means that the weight vector W is the Eigen vector of Eigen problem of “Pair Comparison” matrix A. According to matrix theorem, n is the only non-zero maximum Eigen-value solution. Moreover, if “Pair Comparison” matrix A of the membership functions between attributes are known, then the weight order of the factor (risk) attribute (sub-risk) is correct; because the weight order of the factor attributes is based on the fuzzy assessment of Eigen-value equation of “Pair Comparison” matrix A. The elements of matrix A can be determined by analytic inference or empirical prediction with the powerful tool of fuzzy mathematics for the assessment of related information and problem solving.

6.4.5 Case Study We take the risk evaluation of investment in overseas project as an example, it is an objective events based on five degree of fuzzy classification in I(negligible risk), II(light risk), III(common risk), IV(considerable risk) and V(severe risk). Using “s” as the identifier of following analysis, then the “Pair Comparison” matrix can be expressed as:  s  s E = eij = ubi /ubj (s = I, II, III, IV, V; i, j = 1, 2, 3, 4)

(6.8)

Define investment risk of overseas project as A1, its attribute factors B1 , B2 , B3 , B4 , among them, B1 —Political risk; B2 —Exchange rate risk; B3 —Cash flow risk; and B4 —Credibility risk of project collaborators. Among five grades fuzzy evaluation, the “Pair Comparison” for project risk investment event can be defined as: Rank “1” represents slight importance between the factor and the risk classification; Rank “2” represents common importance between the factor and the risk classification; Rank “3” represents obvious importance between the factor and the risk classification; Rank “4” represents big importance between the factor and the risk classification; and Rank “5” represents severe importance between the factor and the risk classification. The fuzzy membership function can be determined by the assessment of importance for individual attribute with the objective events; or measuring the curve of fuzzy membership function under different cases. If ubi and ubj represent the membership (importance level) of attributes i and j of an objective event A1 , for instance, if (e14 ) = uubjbi = 24 = 2, it means attribute “1” (political risk) and attribute “4” (credibility risk of project collaborators) are “big importance” (Rank “4”) and “common importance” (Rank “2”) for the importance of investment risk. Another example of “Pair Comparison” is:

6.5 Conclusive Remarks

89

⎡

⎤ 1112 ⎢ ⎥ 1132 ⎥ E=⎢ ⎣ 0.33 0.33 1 0.5 ⎦ = (eij )(i, j = 1, 2, 3, 4) 0.5 0.5 2 1

(6.9)

Here we assume (e14 ) = 2, then substitute (6.9) into (6.6) in standard formula: (E − λI) · W = 0

(6.10)

The solutions of (6.10) and (6.9) are: W = {0.33 0.33 0.10 0.24}

(6.11)

λmax = 4.08(> n = 4)

(6.12)

The forming of λmax > n = 4 is due to the incompatibility of matrix E during fuzzy evaluation (6.11) represents the weight series of attributes B1 , B2 , B3 及 B4 .

6.4.6 Practical Treatment of Overseas PPP Project The problem is concerning to the practical treatment of Fig. 6.4 the system analysis and implementation action of overseas PPP project. Suppose, A1 is the original problem of risks in overseas PPP project; its sub-system B1 , B2 , B3 , … should be replaced by humanity recognition HR1(sub-risk), Worthwhile value risk VR2, Financial carrying capacity risk FR3, and Life-cycle PPP project management (1st class) risk LcR6. And in Fig. 6.4, C1, C2, …, C6 should be replaced by the attributes of different sub-risks HR11, HR12, …, HR15 and VR21, VR29; LcR61, LcR66. In above equations, each sub-risk and its attributes should take corresponding weights, which can be obtained through the methodologies mentioned above; or by the “brain storm” from the comprehensive scoring of the experienced experts. Some time, for simplification reason, the solution can be processing by means of neglecting the small weighted sub-risks as well as its attribute factors.

6.5 Conclusive Remarks It is obvious that the solution of risk management for overseas PPP project such a complicated and environmental changeable uncertain fuzzy large system; which is impossible to solve without the involvement of system methodology. Applying the point of view of dialectic unification of two opposites in the system, specifying the advantages and disadvantages factors in the controlling the risks of overseas PPP

90

6 System Dynamics Modeling and Applied to International PPP Project …

C1

B1

C2

C3 A1

B2 C4

B3

Quantitative Fuzzy Evaluation

C5

C6

Fig. 6.4 Simplified FAHP factor framework

project, and maintaining the dynamic balance between fully utilizing the favorable factors and retracting the utilizing the unfavorable factors. Through comprehensive observation and analysis and finding out internal law of changes of the system, the system modeling can be established, which are the basic arguments of system decision making as well as the action in each stages. The uncertain fuzzy big system of risk management of overseas PPP project can be expressed as Fig. 6.4 in a simplified hierarchy framework of system dynamic modeling; which can be solved by corresponding fuzzy analytic hierarchy process (FAHP). As the treatment of practical problem, the problem solving can be further simplified by neglecting several factors or attributes with small weight value. The public–private partnership in infrastructure development is discussed with case studies from Asia and Europe, which is shown in reference (Partnership and in Infrastructure Development: Case Studies from Asia and Europe,Liaoning Press of Science and Technology, (in Chinese). 2010).

References Forrester, J. W. (1961). Industrial dynamics. Cambridge, Massachusetts, USA: MIT Press. Forrester, J. W. (1968). Principles of systems. Cambridge, USA: Wright-Allen Press, Inc. Forrester, J. W. (1969). Urban dynamics. Cambridge, Massachusetts, USA, MIT Press. Forrester, J. W. (1985). Industrial dynamics: A breakthrough for decision makers. Harvard Business Review, 36(4), 37–66.

References

91

Lin, S. (2019a). Investigation to risk management of overseas PPP projects (I). Project Management Review, 24(3) (in Chinese). Lin, S. (2019b). Investigation to risk management of overseas PPP projects (II). Project Management Review, 25(4), 44–47 (in Chinese). Lin, S., & Huang, D. (2019c). Project management under internet era. Springer & SJTU Press. Qifan, W, et al. (2005). Advantages of system dynamics approach in managing project risk dynamics. Journal of Fudan University, 44(2), 201–206 (in Chinese). Richardson, G. P., & Pugh, A. L. (1981). Introduction to system dynamics modeling with DYNAMO. Cambridge, Massachusetts, USA: MIT Press. (2010) Public-private partnership in infrastructure development: Case studies from Asia and Europe. Liaoning Press of Science and Technology (in Chinese).

Chapter 7

Fuzzy TOPSIS Method for the Cost Prediction in Bridge Engineering Project

7.1 Background Information On emphasizing the slogan of “strengthen the nation through transportation”, it seems more and more important for the cost prediction of transportation engineering projects. Nevertheless, due to insufficient of data cumulated, it also brings to the difficulty in prediction of the bridge cost. Therefore, to find a reasonable approach for the cost prediction in bridge engineering should access to the costs of past projects, the available cumulated costs of the past-built bridges are actually possessed practical meaningfulness. The cost prediction of new bridge can be fully beneficial to retrieve information from those costs of the available bridges having built in the past, so as to increase the accuracy of cost prediction and managerial effectiveness, which are practical meaningful to the investment selection as well as the managerial development of transportation engineering projects. It is also meaningful to the reasonable arrangement of the construction planning, prediction of construction budgeting, preparing the capital investment, effectively control and saving project cost, simplifying the procedure as well as increasing the cost prediction and managerial decision efficiencies. Fully utilizing the cost information of historical projects for inducing the information of newly constructed project, i.e.; the engineering cost prediction, which is a process of sample inference and matching. Sample inference is one of the technical investigations in artificial intelligence technology, which is follow to the thinking philosophy of human being. The present research is how to utilize cumulated information to find the high matching samples from available constructed projects and based on which to carry out the comprehensive evaluation and prediction decision of investment alternative. This is also a problem of information matching under uncertainties. There are many ways to solve the problem, among them, fuzzy TOPSIS method is widely to be applied in practice.

© Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 S. Lin and G. Zhao, Fuzzy Quantitative Management, Fuzzy Management Methods, https://doi.org/10.1007/978-981-10-7688-6_7

93

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7 Fuzzy TOPSIS Method for the Cost Prediction in Bridge Engineering …

Using bridge construction investment selection in transportation engineering as an example, applying tri-angle fuzzy membership function and fuzzy TOPSIS method based on close to the degree of fuzzy set, supply the reference alternative for the investment decision making of the newly constructed project through the comparison of data between new constructed project and the old available samples.

7.2 On Fuzzy TOPSIS Method The so-called TOPSIS (Technical for Oder Preference by Similarity to an Ideal Solution) method will be presented in this chapter, which is based on the ordering approach for limited number of evaluation objectives with the nearness degree of the idealized objectives. At the same time, it is an approach for assessing relative advantage or disadvantage in available objectives. As the sorting method close to ideal solution, it requires the utility function to be monotone increasing (or decreasing). Therefore, it is an effective method common used in multi-objective decision analysis.

7.3 Bridge Cost Prediction Review 7.3.1 Investigational Review in the Prediction of Engineering Cost The traditional engineering cost prediction is divided into two categories: deterministic prediction and in-deterministic prediction. For the previous one, it possesses return prediction and time series etc. Many authors have been studied the problem in deep and around different aspects: Such as H. S. Zhou suggests building of the engineering cost prediction model based on stepwise regression (Zhou et al., 2022), F. Yuan builds construction cost prediction method based on time series (Yuan, 2021), N. N. Wang investigates the cost prediction of substation engineering model based on support vector machine (Wang et al., 2016), Yang Yin builds the cost prediction model for power distribution network and its error analysis based on PSO-LSSVM (Yang et al., 2020), Z. G. Hu studies on engineering cost evaluation model base on fuzzy prediction (Hu et al., 1997), and Y. R. Xu Studies civil engineering construction cost prediction based on fuzzy comprehensive evaluation (Xu et al., 2021), L. Y. Teng investigates the building of construction engineering cost prediction model based on BP neural network (Teng, 2020), Q. Liu uses BP and RBF neural network to predict construction cost (Liu et al., 2013), X. Liang develops fuzzy neural network model for cost prediction (Liang et al., 2017), and H. Wang suggests the model for design and implementation of cost prediction (Wang et al., 2019). As using modern technologies to study the problem, Y. H. Chen is using machine learning technology (Chen et al., 2021).

7.3 Bridge Cost Prediction Review

95

At the moment, most project cost prediction is limited in the area of building construction and rare in transportation engineering projects. Since it is difficult to obtain the data from transportation projects due to uncertain management and environmental conditions, which cause the difficulties of direct prediction. However, uncertain prediction by fuzzy theorem can solve this problem with comparative accurate solution by means of fully using matching past similar samples to determine the cost prediction of current project. The sample matching method is widely applied in practice with its correct weight arrangement and the calculation of its similarity. S. H. Jiang is applying sample inference to define the cost prediction (Jiang et al., 2011), by means of text clustering X. Y. Gao studied the recognition of changing characteristics of substation project (Gao et al., 2020), using sample inference and event characteristic table Q. H. Yang studies the working time evaluation (Yang et al., 2007). Moreover, Q. Li studies diagnosis of fault by sample inference method (Li et al., 2007), S. S. Qing uses sample inference to study fire accident statistic indicator system (Qing et al., 2018), J. Yang studies case matching model by Bias network (Yang et al., 2019), X. M. Zhang based on Zhen Hua Heavy Industrial Co. practice, studies service value chain of product by dynamic matching (Zhang et al., 2021), and M. H. Wan is using sample inference to treat multi-similarity matching algorithm modeling problem (Minghai et al., 2021); The more extended application fields are appeared in recent years, such as Y. X. Gao by means of hierarchical weighting method to build similarity decision model of railway tunnel exploration alternative similarity decision model, and developed railway tunnel sample base system based on GIS; using SQL inquiry to realize similarity sample matching (Gao et al., 2021). Table 7.1 represents the variety of methods in engineering cost prediction. Table 7.1 Various methods of cost prediction

Classification

Methodologies

Determine

Regression method Time series SVM and LSSVM (vector machine)

In-determine

Fuzzy method Fuzzy comprehensive evaluation method

Deep learning

BP neural network RBF neural network

Comprehensive methods

Fuzzy + neural network Neural network + SVM

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7 Fuzzy TOPSIS Method for the Cost Prediction in Bridge Engineering …

Table 7.2 Different ideas of sample matching

Classification

Methodologies

Deterministic

Vector space similarity Optimum algorithm of particle group K nearness method Bias network model SQL method

In-deterministic

Event characteristics table

Comprehensive method

Dynamic matching Multi-dimensional similarity matching algorithm

7.3.2 Sample Matching Ideas The sample matching similarity method is divided into deterministic and indeterministic, otherwise it can be comprehensive one. The deterministic sample matching similarity method can be further divided into vector space similarity, optimum algorithm of particle group, K nearness method, Bias network model and SQL method; the in-deterministic sample matching similarity method uses event characteristics table for problem solution. As regard to the comprehensive method, it can be separated into dynamic matching and multi-dimensional similarity matching algorithm. Table 7.2 represents the ideas of sample matching.

7.3.3 Fuzzy Method Fuzzy methodologies can be used for solving hardly determined quantitative problems, TOPSIS method is proper to use in judging the order of quality for reference samples. The combination of fuzzy methodology and TOPSIS method with consideration of multiple evaluation indicators, at the same time making comprehensive evaluation of similarity degree between samples, it will provide more realistic reference for new project investment decision and which have been applied in past practice. Y. Tan et al. using fuzzy TOPSIS approach to study project selection for the contractors (Tan et al., 2010), and O. Taylan et al. are using fuzzy AHP and fuzzy TOPSIS for project selection by means of involving quantitative attributes into performance analysis of construction project and according to the time, cost, quality, safety and environmental sustainability to transfer qualitative data into quantitative equivalent quantitative evaluation standard, and then evaluate 30 construction projects (Taylan et al., 2014), Muhammet Gul et al. use fuzzy AHP decision matrix for fuzzy multi-criteria risk assessment of aluminum industry (Gul & Guneri, 2016), Y. Ge et al. study the fuzzy preference programming and study the intelligent design with

7.4 Method Selection of Cost Prediction

97

Fig. 7.1 Fuzzy method for evaluation

sample base (Ge et al., 2017). From these literature analyses, all the authors identified the indicators of cost prediction, which possess high reference value. for optimum maintenance strategies selection. Gao et al. (2021) based on the water resource allocation evaluation as a sample study comprehensive evaluation of water resource allocation by means of fuzzy combination of empowerment and TOPSIS model. All above mentioned fuzzy evaluation model can be expressed in Fig. 7.1.

7.3.4 Conclusion In this section, we have in detail discussed the methods of cost prediction, selection of indicator, matching ideas as well as the fuzzy methodologies, which provide important reference for the selection of research methods in the future. Under insufficient data situation, it is valuable that the uncertain engineering cost prediction possesses its practical meaningfulness, moreover, applying sample matching and fuzzy methods for cost prediction is feasible and effective. We are using fuzzy TOPSIS method, fuzzy calculated samples with various characteristic importance for building sample base, then empower to TOPSIS method for the sample similarity sorting and providing the reference good order of sample in engineering cost prediction of the new project.

7.4 Method Selection of Cost Prediction 7.4.1 Indicators of Engineering Cost Prediction For the investment selection decision of engineering project, first of all, we need to understand the state in different parts of the project, its internal structure and states as well as it inter-relationship. According to indicator selection in the literatures, combined with the importance and characteristics of bridge structure in the transportation engineering, we chose five dimensions of the cost prediction as cost of foundation, cost of bridge pier, cost of bridge structure, cost of bridge desk and construction period. Figure 7.2 is provided for the study of bridge engineering cost evaluation.

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7 Fuzzy TOPSIS Method for the Cost Prediction in Bridge Engineering …

Fig. 7.2 Dimension structure of bridge engineering cost evaluation

Fig. 7.3 The matching process framework

Figure 7.3 is provided for the matching process framework of the sample. Using fuzzy TOPSIS method to introduce the good order of the sample in the sample base for the cost prediction: Select certain typical samples of engineering profession, based on available information, use fuzzy method to obtain the triangle fuzzy value of the characteristic factors, then obtain weight k ij through defuzzification and building the sample base Rmn , where m represents the number of sample, n represents the number of characteristic factors. When the decision maker through fuzzy inference makes decision to a new project, he/she should proceed matching calculation to each of the sample in the sample base, then selects certain high matching degree samples as the basis to determine the cost prediction of the new project through necessary revision.

7.4.2 Fuzzy Method Apply fuzzy method to determine the importance degree of uncertain characteristic factors; based on structural judge matrix in decision state, according to the importance of characteristic factor, the 1–9 weight degrees are defined, which is the solution of avoiding the difficulties in comparison of factors with different characters.

7.4 Method Selection of Cost Prediction

99

For qualitative information, the triangle fuzzy number method is used for expert scoring, the triangular is written by k ' = (a, b, c), where b represents the maximum value, and a, c represent the lower and upper bounds respectively as shown in Fig. 7.4. Using triangular fuzzy number to replace the assignment in AHP is shown in Table 7.3. If there are x experts in scoring, then the triangular fuzzy number can be expressed by ∑ ) c , , x x x

(∑ K =

a

∑

b

(7.1)

Similar to AHP, calculate each weight of characteristic fuzzy number of triangular number according to judgment matrix. For one sample, the triangular fuzzy weight of ith indicator is ki' = (kai , kbi , kci ), among them, kai is determined by (7.2), kbi and kci as the same: Fig. 7.4 Triangular fuzzy number diagram

Table 7.3 The importance of weight determination of triangular fuzzy number

Definition

Exact number

Triangular fuzzy number

Same importance

1

[1,1,2]

Slight importance

3

[2,3,4]

Importance

5

[4,5,6]

Obvious importance

7

[6,7,8]

Very important

9

[8,9,9]

Middle important

Middle value

Corresponding fuzzy number

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7 Fuzzy TOPSIS Method for the Cost Prediction in Bridge Engineering …

∑n

j=1 ai j ∑n i=1 j=1 ai j

kai = ∑n

(i = 1, 2, . . . , n)

(7.2)

Using weighted average method for defuzzification of triangular fuzzy number, furthermore, applying characteristic important degree k i as the evaluation indicator and involve it into sample base as following (7.3): ki =

kai + 2kbi + kci 4

(7.3)

7.4.3 Fuzzy TOPSIS Method During the calculation of sample matching priority degree we use fuzzy set close degree as the matching indicator, each row in the sample base as fuzzy set M, then use N(A, B) to represent the close degree between fuzzy set A and B, to describe the similarity degree between sample A and B. Utilize variation coefficient to offer weight for evaluation indicators respectively, empowering weight according to the difference of data variation, higher weight for higher difference of data variation and verse versa, so as to distinguish different samples. Denote the variation coefficient vj of the ith indicator is shown in (7.4), S j represents standard difference, x j represents average value: vj =

Sj , ( j = 1, 2, . . . , n) xj

(7.4)

Using close degree formula to calculate the nearness degree N(A, B), if fuzzy set M = {k1 , k2 , . . . , kn }, then the nearness degree can be expressed by (7.5): ) 21 ) n 1 ∑ N ( A, B) = 1 − √ (A(ki ) − B(ki ))2 n i=1

(7.5)

There are two cases in matching nearness degree of each sample with new sample, suppose a represents the lowest satisfactory degree: (1) If sample which satisfies nearness degree N(A, B), i.e., the nearness degree ≥a, then accept it for the reference of project investment during the investment selection of new project; (2) If sample which cannot be satisfied nearness degree N(A, B), i.e., N < a, then abandon the acceptance and return to new fuzzy matching. Finally, carry out nearness degree score sorting for the reference sample, and obtain the priority order of engineering project investment selection reference sample.

7.5 Case Study

101

7.5 Case Study 7.5.1 Data Treatment Applying available bridge data base materials to analyze the new building bridge engineering and carry out the priority sorting of the reference sample for providing reference of investment selection. The data from sample base is presented in Table 7.4 with the data of new invested bridge project. Standardize data of Table 7.4 by min–max standardization, we obtain Table 7.5. Table 7.4 Data from sample base and new sample No.

Bridge serial number

Cost of foundation (Million)

Cost of pier (Million)

1

ZSMRD

2.39

2.39

2

DXRB

6.904

3.55

3

XQRB

1.86

4

TDRB

5

KJRB

6

Cost of bridge (Million)

Cost of deck (Million)

Price (Million/M3 )

6.048

0.985

14.066

8.99

1.46

20.921

0.958

0.223

0.394

5.635

7.542

3.885

9.828

1.60

22.855

3.00

1.239

3.49

0.53

8.259

TJRB

9.765

4.185

11.44

2.511

27.900

7

SYJRB

3.304

1.416

3.965

0.755

9.940

8

JGRB

4.805

2.475

6.26

1.019

14.560

9

New project

4.44

2.287

5.874

0.942

Table 7.5 Results after standardization No.

Bridge serial number

Cost foundation

Cost of pier

Cost of bridge

Cost of deck

1

ZSMRD

0.067

0.444

0.519

0.279

2

DXRB

0.638

0.803

0.782

0.504

3

XQRB

0.000

0.000

0.000

0.000

4

TDRB

0.719

0.907

0.856

0.570

5

KJRB

0.144

0.087

0.291

0.064

6

TJRB

1.000

1.000

1.000

1.000

7

SYJRB

0.183

0.142

0.334

0.171

8

JGRB

0.373

0.470

0.538

0.295

9

New project

0.326

0.412

0.504

0.259

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7 Fuzzy TOPSIS Method for the Cost Prediction in Bridge Engineering …

7.5.2 Calculation Results The calculation of weight through variation coefficient method is presented in Table 7.6. Empowering the normalized data with certain weights, then calculate fuzzy nearness degree and sorting, we may obtain reference priority order as in Table 7.7. It is noticed that sample 8 possesses higher reference meaning with the new bridge, the nearness degree of sample 4 and 6 are lower than the threshold value, so it should be abandoned. The final reference priority investment selection order will be sample 8 > sample 1 > sample 7 > sample 5 > sample 2 > sample 3. The real cost of selecting sample 8 is 14.56 Million/M3 , which can be used as the engineering cost prediction for newly built bridge. Table 7.8 is the revised cost of the bridge: Table 7.6 Weight calculation by variation coefficient method Cost of foundation

Cost of pier

Cost of bridge

Cost of deck

Average number

4.89.0

2.488

6.235

1.133

Standard difference

2.660

1.177

3.475

0.647

Variation coefficient

1.839

2.115

1.794

1.751

Weight

0.25

0.28

0.24

0.23

Table 7.7 Results of nearness degree calculation of samples No.

Bridge serial number

Result of nearness degree

8

DXRB

0.976

1

TDRB

0.935

7

JGRB

0.905

5

SYJRB

0.878

2

KJRB

0.839

3

TJRB

0.805

4

ZSMRD

0.797

6

XQRB

0.685

Table 7.8 Revised calculation of similar sample Cost of foundation (M)

Cost of pier (M)

Cost of bridge (M)

Cost of deck (M)

JGRB

4.805

2.475

6.26

1.019

New project

4.44

2.287

7.4

0.942

Variation rate

0.924

0.95824

0.938

0.924

Weight

0.25

0.28

0.24

0.23

7.5 Case Study

103

The calculated average variation rate of the bridge is 0.928, then the investment offer will be 14.56. ∗ 0.928 = 13.512 (Million/M3 ) with error rate 14.077 − 13.512 ∗ 100 = 4.1. 13.512

7.5.3 Sensitivity Analysis The sensitivity analysis is proceeded for the purpose of evaluating the influence degree of prediction by each indicator, Fig. 7.5 shows the sensitivity analysis of nearness similarity degree if one deletes one from the four indicators. Figure 7.6 shows the comparison of results of prediction. It is shown in Figs. 7.5 and 7.6 that no matter selecting any three indicators, sample 8 possesses very high nearness degree, the reason is that for every indicator, sample 8 has very little difference from that of the new project; without consideration of foundation cost, sample 1 has similarity degree approaches to 98.6%, the final prediction is 13.521 Million/M3 , which is very close to the real offer. For other nearness degree, sample 1 has had more similarity with the new project, thus it is valuable to be referred on. Finally, the sensitivity difference for four indicators is limited and doesn’t influenced too much in the final results, thus the main reason of difference is come from the data uncertainty of the samples.

Fig. 7.5 Sensitivity analysis of nearness degree

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7 Fuzzy TOPSIS Method for the Cost Prediction in Bridge Engineering …

Fig. 7.6 Comparison of prediction

7.6 Conclusive Remarks Utilizing uncertain triangular fuzzy data to establish sample base and using variation coefficient method to realize weight re-distribution, then evaluate nearness degree between samples by means of Euler’s distance and based on which to carry out TOPSIS evaluation sorting, then finally obtain the prediction cost of offer through sample regulation. Different from majority of cost prediction, this chapter develops how to using fuzzy TOPSIS method for predicting the bridge construction cost, this method can be used under the condition of less information or under the condition of mass uncertain information. As long as cost prediction, the sample base can be further expanded and renewable, thus it benefits to simplifying the procedure, control the engineering cost and increasing the decision efficiency as well as its exactness. The fuzzy TOPSIS method for predicting engineering cost can be properly used in the bidding offering of transportation engineering, its result shows the final error is limited around 5% and satisfied to the engineering requirement. The fuzzy inference method is expected to be the powerful and supporting tool in decision system under less information and uncertain environment.

References

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References Chen, Y., et al. (2021). Studies of industrial construction cost prediction by means of machine learning. Journal of Wuhan University of Science and Technology (Information and Managerial Engineering Version), 43(04), 314–321. (in Chinese). Gao, X., et al. (2020). The studies on recognition of changing characteristics of substation project based on text clustering. Journal of Building Economy, 41(S2), 200–203. (in Chinese). Gao, Y., et al. (2021). The intelligent design method of excavate alternative for railway tunnel based on sample base. Journal of Railway Construction, 61(10), 7–12. (in Chinese). Ge, Y., et al. (2017). An integrated logarithmic fuzzy preference programming based methodology for optimum maintenance strategies selection. Journal of Applied Soft Computing, 591–601. Gong, Y., et al. (2021). The comprehensive evaluation method of water resource allocation alternative by means of fuzzy combination empowerment. Journal of Statistics and Decision, 37(13), 179–183. (in Chinese). Gul, M., & Guneri, A. F. (2016). A fuzzy multi criteria risk assessment based on decision matrix technique: A case study for aluminum industry. Journal of Loss Prevention in the Process Industries, 40, 89–100. Hu, Z., et al. (1997). Studies on engineering cost evaluation model based on fuzzy prediction. Journal of System Engineering Theorem and Practice, 17(2), 51–56. (in Chinese). Jiang, S., et al. (2011). Studies of investment evaluation system of construction projects based on sample inference. Journal of Building Economy, S1, 66–70. (in Chinese). Li, Q., et al. (2007). The sample inference method and its application in fault diagnosis of airplane. Journal of Beijing Aeronautic/Astronautic University, 05, 622–626. (in Chinese). Liang, X., et al. (2017). On the construction engineering cost prediction model based on fuzzy neuro-network. Journal of Technical Economy, 36(03), 109–113. (in Chinese). Liu, Q., et al. (2013). Construction engineering cost prediction model of Xiamen municipality by means of BP and RBF neuro-network. Journal of Overseas Chinese University (Natural Science Version), 34(05), 576–580. (in Chinese). Qing, S., et al. (2018). The studies of simulation degree of fire accident and its counter-measures based on sample inference. Journal of Safety and Environmental Engineering, 25(05), 150–155. (in Chinese). Tan, Y., Shen, L., Langston, C., et al. (2010). Construction project selection using fuzzy TOPSIS approach. Journal of Modelling in Management. Taylan, O., Bafail, A. O., Abdulaal, R. M. S., et al. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105–116. Teng, L. (2020). Investigation of construction engineering cost prediction model building based on BP neuro-network. Residential Industry, 12, 110–113. (in Chinese). Wan, M., et al. (2021). Decision of intelligent workshop treatment based on reduced sample inference. Journal of Mechanical Engineering, 32(20), 2458–2467+2491. (in Chinese). Wang, N., et al. (2016). Studies on cost prediction of substation engineering based on support vector machine. Journal of Building Economy, 37(No.403(05)), 48–52. (in Chinese). Wang, H., et al. (2019). The design and implementation of cost prediction model in construction engineering. Journal of Micro-computer Applications, 35(08), 95–97+104. (in Chinese). Xu, Y., et al. (2021). Studies on cost prediction of civil engineering construction based on fuzzy comprehensive evaluation. Fujian Construction Materials, 12, 104–106. (in Chinese). Yang, Q., et al. (2007). Working time evaluation method of parts by means of sample inference and event characteristic table. Journal of Mechanical Engineering, 05, 99–105. (in Chinese). Yang, J., et al. (2019). Network public opinion case matching model based on Bias network. Journal of Modern Information, 39(10), 94–101. (in Chinese). Yang, Y., et al. (2020). Building of cost prediction model for power distribution network and its error analysis based on PSO-LSSVM. Journal of Automation Technology and Applications, 39(02), 98–102. (in Chinese).

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Yuan, F. (2021). Building construction cost prediction method based on time series. Journal of Building and Budgeting, 11, 35–37. (in Chinese). Zhang, X., et al. (2021). The building path of equipping service value chain for manufacturing enterprises under the view point of dynamic matching. Journal of Soft Science, 35(04), 76–82. (in Chinese) (2007). Zhou, M., et al. (2022). Building of the engineering cost prediction model based on stepwise regression. Journal of Automation Technology and Applications, 41(03), 162–166. (in Chinese).

Chapter 8

The Advantages of Quantitative Management in Decision

8.1 Two Kinds of Events with Different Nature and Its Modeling There are two different kinds of uncertainties in the objective world: the uncertainty in occurrence of uncertain event; as well as the uncertainty in degree of the event. Though there are many uncertain events treated by human being have been used by means of probability theoretical modeling, nevertheless, it is just suitable for those events, which are occurred under uncertain in its occurrence. For the majority of managerial events, once it have been taken for investigation, there will be doubtlessly a realistic event, from the strict physical point of view, it is no longer a probably existing event, but a real event with uncertain in degree. Therefore, it is unsuitable to use probability model for its investigation. Since so-called project management can be understood as “Oriented to certain purposes and within limited resource, to make a series of decision under uncertain environment”; on the other hand, decision making is the highest intelligence of human being, which can be simulated through artificial intelligence (AI) in digital form by means of computer; moreover, the uncertain managerial environment will force the decision maker to instantaneously processing mass fuzzy information during fuzzy inference. Since by means of “Fuzzy-AI Model”, using AI technology through computer, to processing fuzzy inference of human being can be straightforwardly realized; therefore, building a “Fuzzy-AI Model” combining both fuzziness and AI seems reasonable and necessary. The functions of “Fuzzy-AI Model” are particularly suitable to simulating the processes of decision maker who uses computer to simulate the fuzzy inference during the processes of managerial decision making for solving practical real world problems. This model is effective in quantitative management of project, and which will play a new scenario in the discipline of managerial science. We need thoroughly to investigate the theoretical framework of this model and expanding its application fields for solving practical problems. © Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 S. Lin and G. Zhao, Fuzzy Quantitative Management, Fuzzy Management Methods, https://doi.org/10.1007/978-981-10-7688-6_8

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8.2 Theoretical Basis and Application Areas of “Fuzzy-AI Model” During the treatment of uncertain events, there are two reasons that people accept probability theorem rather than the fuzzy theorem: Firstly, the probabilistic density functions are continuous analytic function and mathematically derivable; but the fuzzy membership functions are selected arbitrarily by subjected information of the decision maker. Secondly, the parameters of probability density function, such as middle value and square variance of normal distribution function, are obtained through the statistics of real practical observation, which represent practical reality; nevertheless, the membership function of fuzzy method is determined by subjective information by the experts, its objectiveness is quite questionable. The author of this book has already worked in this area for years and has pointed two points of view in reference (Lin, 2005): (1) For the purpose of covering the unsatisfactory in its mathematical modeling, the fuzzy theorem can be fully expressed by continuous and derivable analytic function as its fuzzy membership function; at the same time, the parameters of its fuzzy membership function can be determined through machine learning of AI technology from massive practical sample for approaching to the real situation. Therefore, both probability theorem and fuzzy theorem have had the same rationality both from mathematical theorem and from practical coincidence points of view. (2) For the solution of massive managerial problems, which possess degree uncertainties, so if one without thorough consideration to apply probabilistic methods; it seems paradox in its physical nature, since which is only suitable in solving uncertain in occurrence problems. In conclusion, it is unreasonable that mathematical modeling cannot compatible with the physical nature. Therefore, for the majority of daily uncertain in degree managerial problems, it is reasonable to use corresponding uncertain mathematical models, such as fuzzy theorem, or grey theorem, etc. Table 8.1 shows the application range of “Fuzzy-AI Model”, which is corresponding to the eight items in project life-cycle as well as the ten knowledge in PMBOK (Project Management Body of Knowledge) of PMI (Project Management Institute) (PMI, 2016; Zadeh et al., 1975) to form 10 × 8 = 80 research areas 1-1, 1-2, 1-3, … 2-1, 2-2, 2-3, … etc. It is cleared that the ranking of management in different stages of the life-cycle of the project development.

8.2 Theoretical Basis and Application Areas of “Fuzzy-AI Model”

109

Table 8.1 Ranking of management in different stages of project development Column

Managerial issues

Development stages of project life-cycle 1

2

3

4

Design planning

Project scheduling

Project implementation

Project control

Row

Managerial operation

1

Comprehensive management

1-1

1-2

1-3

1-4

2

Area management

2-1

2-2

2-3

2-4

3

Time management

3-1

3-2

3-3

3-4

4

Cost management

4-1

4-2

4-3

4-4

5

Quality management

5-1

5-2

5-3

5-4

6

HR management

6-1

6-2

6-3

6-4

7

Communication management

7-1

7-2

7-3

7-4

8

Risk management

8-1

8-2

8-3

8-4

9

Procurement management

9-1

9-2

9-3

9-4

10

Stakeholder management

10-1

10-2

10-3

10-4

Column

Managerial issues

Development stages of project life-cycle 5

6

7

8

Organizational innovation processes

Maturity assessment of enterprise

Assessment of process test

Team cooperation

Row

Managerial operation

1

Comprehensive management

1-5

1-6

1-7

1-8

2

Area management

2-5

2-6

2-7

2-8

3

Time management

3-5

3-6

3-7

3-8

4

Cost management

4-5

4-6

4-7

4-8

5

Quality management

5-5

5-6

5-7

5-8

6

HR management 6-5

6-6

6-7

6-8

7

Communication management

7-6

7-7

7-8

7-5

(continued)

110

8 The Advantages of Quantitative Management in Decision

Table 8.1 (continued) Column

Managerial issues

Development stages of project life-cycle 5

6

7

8

Organizational innovation processes

Maturity assessment of enterprise

Assessment of process test

Team cooperation

Row

Managerial operation

8

Risk management

8-5

8-6

8-7

8-8

9

Procurement management

9-5

9-6

9-7

9-8

10

Stakeholder management

10-5

10-6

10-7

10-8

8.3 Some Modeling Expressions of Quantitative Management Table 8.1 can be expressed by a 10 × 8 matrix E, which represents the concerning domain of investigation for the managerial event: E=

m  k    eij (i = 1, 2, . . . , 10; j = 1, 2, . . . , 8)

(8.1)

i=1 j=1

Among them, each element eij of E represents a concrete managerial event or decision event, which can be expressed by following mathematical models.

8.3.1 MP (Mathematical Programming) Model It is an optimum model based on mathematical programming. Define the fuzzy variables include both determined and in-determined one, such as the cost management of metro wagon repair (Lin et al., 2009) will belong to 4-1 event in Table 8.1; its mathematical programming model can be expressed by Max (Min) Objective function Fi (X)(i = 1, 2, . . .) Under constrain conditions Ck (k = 1,  2, . . .) To find: X = xj (j = 1, 2, . . .)

(8.2)

The solution of decision model (8.2) is variables X = {xj } (j = 1, 2, …), which are the supporting parameters of decision making. Since before modeling process

8.3 Some Modeling Expressions of Quantitative Management

111

we have treated the uncertain decision parameter X = {xj } in fuzzy quantitative inference, then we may process the decision model (8.2) as a normal optimization solution problem. As regard to the overseas engineering project management (Xu & Lin, 2014), its risk management relates to all of the risks from 8-1 until 8-8 in the Table 8.1.

8.3.2 NM (Nearness and Matching) Model NM model is to compare the parameters of management event with the parameters of typical bench-marking one for obtain the optimum settlement. Such as the pricing problem of real estate business (Chen & Lin, 1995), which is defined in issue 9-1 of Table 8.1; if P is the price of the house to be sold, which depends on 10 different environmental factors, namely: Geographic position, structural type, inner/external decoration, number of layer, need/supply situation of real estate market, potentiality of price increasing of real estate market, traffic convenience, status of auxiliary facilities, surrounding environment and political economic situation. The 10 environmental factors is u m (m = 1, 2, 3, …, 10); define the membership function of each factor μ (u m ); and take 10 similar samples to form the sample base, then the fuzzy vector of 10 samples Aj (tm ) is μ(u m ) μ(u 1 ) μ(u 2 ) + +···+ u1 u2 um (8.3) (influence factor m = 1, 2, . . . , 10; sample number j = 1, 2, . . . , 10)

Aj (tm ) =

Here μ(u m ) is the fuzzy membership function of influence factor t m . It is mentioned that in (8.3) the sign “+” means “and” rather than “add”; therefore, (8.3) represents the fuzzy membership functions of 10 influence factors of 10 samples; at the same time, the fuzzy vector of price of the house B(ti ) can be expressed by: B(tm ) =

μ B (u m ) μ B (u 1 ) μ B (u 2 ) + +···+ B (m = 1, 2, . . . , 10) u1 u2 um

(8.4)

The essential of NM model is using Bj (ti ) as a sample, and form a new sample A

set of ∼k (k = 1, 2, … 10, 11), by putting the elements of Aj (ti ) of 10 samples A

A

A

together; then arbitrary take any two samples ∼p and ∼q from ∼k and calculate its fuzzy distance as: m      D A A A A ∼ ∼ p , ∼q = wi (di ) ∗ di ∼ p , ∼q i=1

(p = 1, 2, 3, . . . , 10, 11; q = 1, 2, 3, . . . , 10, 11; influence factor m = 10) (8.5)

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8 The Advantages of Quantitative Management in Decision

Here: d i (i = 1, 2, …, 10) is the fuzzy distance of influence factors of two samples As shown in (8.6) di = [μp − μq ]i (i = 1, 2, . . . , 10; p, q = 1, 2, 3, . . . , 10, 11)

(8.6)

Consequently, (8.5) is the i-th influence factor after weight wi (di ). . Having (8.6) we may obtain three sample prices of the house being sold r, s, and t, which have min. fuzzy distance and the prices of P(r), P(s) and P(t); After defining the error threshold θ j we also may define the house price P through P(r), P(s) and P(t).

8.3.3 Max/Min Indicator Model The Max/Min Indicator Model is widely applied in the decision making of project management, it pursuits the maximum or minimum of the system parameters for the managerial event. Since the system parameters are the main arguments for the decision making of the managerial event, which implicitly reflect the complex relationship between various influence factors of the event, such as the internal rate of return IRR, the maximum investment revenue rate of the project, the minimum investment return period, etc. If B is system parameter, which is quantitatively evaluated by fuzzy inference. ∼ Define the influence factor set of the system parameter U is: ∼ U = (u1 , u2 , . . . , um ) ∼

(8.7)

Here, u1 , u2 , …, um are the influence factors, then the fuzzy assessment of system to the subset of each influence factors can be expressed by the fuzzy parameter B ∼ satisfactory membership function as shown in (8.8):   μ∼B (u 1 ) μ∼B (u 2 ) μ∼B (u m ) = μ B + +···+ ∼ u1 u2 um

(8.8)

If we take pi (i = 1, 2, …, m) as the weights of the influence factors ui (i = 1, 2, m  pi ∗ μ B (u i ); …, m), then the fuzzy comprehensive assessment indicator is η = i=1

and the Max/Min model should satisfies (8.9): Max/Min η =

m 

pi ∗ μ B (u i ) ≥ I

(8.9)

i=1

Here I is the threshold of satisfaction of the resolution of the managerial event. If (8.9) can be satisfied, then the solution is acceptable. Moreover, as the increasing

8.3 Some Modeling Expressions of Quantitative Management

113

(or decreasing) of the threshold I, the satisfaction of (8.9) means more favorable (or unfavorable) solution of the managerial event can be achieved.

8.3.4 AE (Assessment and Evaluation) Model The fuzzy assessment (or evaluation) model can be a successful decision support to the complicated managerial events. For instance, one may use FAHP (Fuzzy Analytic Hierarchy Process) for the success or failure of the enterprise operation; In addition, for the joint-ventured enterprises, facing the cultural conflicts of common working people with different cultural background, the quantitative risk assessment can be achieved through “Fuzzy-AI” model or other heuristic knowledge. For instance, the fuzzy membership function μ can be established correspondingly through different environment conditions and different cooperative modes. Table 8.2 represents the cooperation modes and cooperation conditions of joint-ventured enterprises. (1) Figure 8.1a represents fuzzy membership functions μa , which varies with human factor influenced cooperative conditions, and it is suitable to • • • •

Fuzzy membership function for favorable economic policies; Fuzzy membership function for infrastructure condition; Fuzzy membership function for stability in political situation; Fuzzy membership function for working efficiency of local staffs.

(2) Figure 8.1b represents fuzzy membership functions μb , which shows the market venue and opportunity in profits; it maintains in constant no matter there is any Table 8.2 The cooperation modes and conditions of joint-ventured enterprises Coop. Cond.

Favorable economic policies

Infrastructure conditions

Stability of Political situation

Market and the opportunity for profits

The working efficiency of local staffs

Foreign investment

●

●

●

●

●

Sino-Foreign joint venture

●

●

●

●

●

Coop. Modes

Local invest with loan from outside

●

Local invest with loan from outside and inside

●

114

8 The Advantages of Quantitative Management in Decision

local and foreign loans. (a)

µ

1.0

(b)

a

µ

b

1.0

0

0

1

2

3

4

Cooperative

1

2

3

4

Modes

Cooperative Modes

Fig. 8.1 Fuzzy Membership functions under different cooperative modes

human factor in act, and remain unchanged along the different cooperative modes. The horizontal axis represents four cooperative modes: • • • •

Mode 1: Foreign ventured; Mode 2: Sino-foreign joint ventured; Mode 3: Local investment with foreign loan; Mode 4: Local investment with local and foreign loans.

Besides, μa is sensitively changed by the variation of cooperative modes; in the foreign ventured mode 1 and Sino-foreign joint ventured mode 2 the μa is approached near 1.0; which show mode 1 and mode 2 are favorable in economic policies, infrastructure conditions, stability in political situation and working efficiency of local staffs. As for local investment with foreign loan the mode 3 and local investment with local and foreign loans the mode 4, due to unfavorable conditions, the fuzzy membership function μa is approached to zero. However, in Fig. 8.1b the fuzzy membership functions μb , which shows the market venue and opportunity in profits, still remain 1.0 when it travels through the areas of four different cooperative modes. It means if the market venue and opportunity in profits are favorable, μb is not sensitive to all the cooperative modes. From the fuzzy membership functions presented in Fig. 8.1, we may quantitatively make the operational decision of the joint ventured enterprises according to these fuzzy membership functions; similarly, we may evaluate and decide the acceptance degree for these joint ventured enterprises.

8.4 Quantitative Management Perspectives In the area of managerial science, there are two kinds of management methodology, namely: qualitative (empirical) management and quantitative (digital) management. As the ever complexity of huge project, people finds out that especially for the risk management, the qualitative management based on the “Heuristic Knowledge” of

8.5 Conclusive Remarks

115

minority of people is far from enough to dealing with the instantaneous seriously changeable project risk environment. Under such situation, the quantitative (digital) management is then naturally on the stage and it plays sophisticated role for the management science. How to realize the digital quantitative management? How to planning its research fields and the scheduling its orientations of future development? This is what we need to answer in this session. Here we will introduce the Fuzzy-AI model (Lin, 2008), which is based on fuzzy inference and artificial intelligence (AI), and combine it with each other. Later, we will discuss the physical essentials as well as the availability of this model to solve various practical real world problems with practical evidence. In order to popularize quantitative management and raise the interests and concerns to our professional colleagues, in the previous chapter, we have discussed the problem in details.

8.5 Conclusive Remarks In this chapter the initiation and development of “quantitative management” are discussed, the theoretical framework as well as the philosophical foundation of it have analyzed in detail. It is focused on the problem in core paradox of “management decision making under uncertain conditions”, i.e. For the majority of management decision problems with degree uncertainties, it is unreasonable and violate to the essentials of its physical nature as we use the probabilistic methods with uncertain in its occurrence? The author regards that, for the majority of management problems, its nature are uncertain in degree, so it seems necessary to build the mathematical model by using fuzzy sets or by a set of grey theorem. Managerial decision making is the highest intelligent activities of the managers, under complicated and instantaneous changes of the project environment, the limited individual intelligence will not be able to cover all the complexities of environmental changes; therefore, the “quantitative management” as well as its intelligentization, digitization and networking will become the only outlet. Through computer artificial intelligence to simulate the intelligence of human being, through network communication to integrate different information and through digitization to raise the level from qualitative to quantitative, it doubtlessly will greatly promote the development of managerial science. The “Fuzzy-AI model” presented in this chapter can accommodating to different attributes of the event and then provide an efficient implementable tool to the “quantitative management”. For the purpose of development areas of “quantitative management” as well as to explore its future development space, Table 8.1 and Eq. (8.1) present the areas of future development. Among them, each area possesses an individual managerial decision making event, which can be modeled by in-deterministic mathematical modeling. Although, these areas are far from comprehensive, which need to be completed through massive research works in the future, nevertheless, the “Fuzzy-AI model” can be accommodating to various quantitative management problems.

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In this chapter several modeling expressions of “quantitative management” are concluded for its application of the readers, we expect it can be successively improved through later investigation and by the development of the quantitative management.

References Chen, H., & Lin, S. (1995). The applications of intelligent decision support system for real estate projects. In Proceedings of the 6th Conference in Computer Applications of Civil Engineering in China (pp. 43–50) (in Chinese) Lin, S. (2005). On paradox of fuzzy AI modeling: Supervised learning for rectifying fuzzy membership functions. Journal of Artificial Intelligence Review, 23, 395–405. Lin, S. (2008). Fuzzy-AI model for managerial science, keynote plenary speech of the 3rd PMI World Research Conference, July 13–15, Warsaw, Poland. Lin, S., Zheng, H., Hu, H., & Yan, J. (2009). Modeling for optimization of long-term metro vehicle repair (Vol. 6, pp. 459–463). In IEEE Computer Society, Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD2009). 14–16 August, Tianjing, China. PMI (2016) A Guide to the Project Management Body of Knowledge (PMBOK Guide) (6th ed). Xu, F., & Lin, S. (2014). Fuzzy-AI model for risk analysis of overseas engineering projects. Journal of Optimization of Infrastructural Management, 26(98) No.1, 2–7 (in Chinese) Zadeh, L. A., Fu, K. S., et al. (1975). Fuzzy Sets and Its Application—Cognitive and Decision Processes. Academic Press.

Chapter 9

On Fuzzy Method of “Deep Knowledge” and “Deep Data” in Project Quantitative Management

9.1 The Background Information At present, the conventional database (DB) and knowledge base (KB) can’t fully meet the needs of decision-making for management problems. Because only the Shallow Data and Shallow Knowledge provided by conventional decision support system (DSS) usually cannot satisfy the requirement of reflecting the deep relationship of various management problems. Due to the lack of corresponding “Deep Data” and “Deep Knowledge”, management decision-makers may confuse during decisionmaking process. Therefore, this chapter forwards the application of fuzzy reasoning in fuzzy approximation as well as the fuzzy similarity in decision space to derive deep data and deep knowledge for illustrate the application of this method through the example of contractor bidding process by deep data. We understand that different attributes of data are required by people in different post. A professional staff concerns only those data which are directly related to his (her) department. However, the department head will concern data much in the inter-departmental nature. Eventually, for the General Manager of a firm, who are responsible to the strategic planning will pay his (her) concerns on more integrated information for decision making combined with his (her) personal experience and judgment. Certainly, the conventional, trivial and tedious “shallow data” and “shallow knowledge” will not be satisfied in this case. What is needed for an efficient decision supporting system (DSS) can be listed as below: Capacity of information re-production supported to the decision making; Availability of existing DB and KB technologies for the DSS with friendly users’ interfaces; Potentiality of manipulating data-information-knowledge as a whole based on unified theoretical basis for enhancing and facilitating system capacity for decision making support.

© Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 S. Lin and G. Zhao, Fuzzy Quantitative Management, Fuzzy Management Methods, https://doi.org/10.1007/978-981-10-7688-6_9

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As a matter of fact, the essentials of above mentioned efficient DSS is a problem of fully utilizing the techniques of artificial intelligence into the system for obtaining deep data and deep knowledge on which a sufficient and rational decision can be made. There are two points to be cardinally concerned, namely: method of representation and inference strategy. The previous one relates to the expression of a complicated causal event. However, the latter one pertains to problem solving by indirect searching or indirect reasoning. Therefore, the fine essence of AI can be likely concluded as to simulate the human intelligence for solving complicated real world problems through rational reasoning process. From the point of view of space of state, based on the inference (searching) strategy the initial state–-a known condition–-is being changed into a series of intermediate states. Eventually, the inference strategy of AI is to find a simplest approach from the initial state to the final objective state is not necessarily to be unique, however, among those feasible objective states, each one represents a feasible solution to the problem. What we’re concerned is that the aims of AI strategy is to manipulate the movement of the intermediate states in order to achieve an optimum path of inference. In general, the system, aimed by its system goal, can be modeled by a set of factor constitutively structured by certain links between them to form a factorial network which represents the essential of system behavior. The nodes of which represent the factors concerned. The weight distribution between factors located at the same level determined by the Eigen-value problem of “pair comparison” relation matrix. Successively manipulating the weight distribution of the factor in each level until the fuzzy assessment of system response for decision making consultation is made. Another problem pertaining to the “intelligentization” of the system is machine learning. A fuzzy learning model with fuzzy input and output is presented, which is essentially a combination of AI technique with the information and system engineering methodologies based on unified mathematical approach. In order to explain the reasoning and learning processes in a complicated decision system, the hierarchical space of state is introduced for representing the internal relationship of the event in different level and in different aspect. Thus, the theoretical manipulation of reasoning could be performed in a unified approach in mathematical explicit. By means of hierarchical space of state the network of fuzzy reasoning both for assessment and learning can be formulated on a rather reasonable basis in its development.

9.2 Representative Expression of Hierarchical Space of State 9.2.1 Multi-Layered Hierarchy Space For a complicated system, the mathematical model is based on the classification of event with different attributes, which behave in different structural models such as tree

9.2 Representative Expression of Hierarchical Space of State

119

Fig. 9.1 Multi-layered hierarchy space

structure and network structure etc. The representation of knowledge or information of an event, no matter in production rule, semantic network or other framework, needs a media of its state description. The compositional hierarchy of the event implies the rationality of introducing the hierarchical space for representing and manipulating the inter-relationship of the sub-events in a hierarchical structure of the complicated event (Saaty, 1980). Further detail description of multi-layered hierarchy space is shown in Lin and Huang (2019). Figure 9.1 shows a two-layered hierarchical space, where: ψ = {ti } represents top level space of state with i = 1, 2, …, n ψ1 = {t1 } = l1j , …; ψi = {ti } = lik , …; ψn = {tn } = lnp , … represent lower level spaces of state with j = 1, 2, … nm1 , k = 1, 2, … nmi , and p = 1, 2, … nmn respectively. Therefore, ψ = {t1 , t2 , …, tn } = (l11 , … l1j , … l1m1 ), … (li1 , … lik , … limi ), … (ln1 , … lnp , … lnmn ) with total dimension of n ∑

mh

(9.1)

h=1

The relation between top and lower states can be expressed by ( ) ψ1 = f 1 l11 , . . . , l1 j , . . . , l1m1 ψ2 = f 2 (l21 , . . . , l2c , . . . , l2m2 ) ····················· ψi = f i (li1 , . . . , lik , . . . , limi ) · · · · · · · · (· · · · · · · · · · · · · ) ψn = f n ln1 , . . . , lnp , . . . , lnmn or Ω = ( f1 , f2 , . . . , fi , . . . , fn ) In state expression, for top space event

(9.2)

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9 On Fuzzy Method of “Deep Knowledge” and “Deep Data” in Project …

(It , Ot , G t ) i.e. → It Ot G t

(9.3)

for lower space sub-event (

) Il(i ) , Ol(i) , G l(i) → Il(i) Ol(i) G l(i)

(9.4)

where It , Gt are initial state and objective state of lower level sub-event. For (9.2) Ω is inter-level transform function for studying the operational relationship between top level operation Ot and the i-th lower level operation of the sub-event O(i) t , here we understand i = 1, 2, … n. It is possible to chart Fig. 9.1 as a network structure rather than a tree or having multi-layered hierarchy space of state if necessary.

9.2.2 Mathematical Distance in Space Based on the space of state, the different between two events A and B in the same attributive subspace can be measured by the generalized mathematical distance D. Define D(A, B) =

m ∑

ωi (di )di (A, B)

(9.5)

i=1

where m, the dimension of (sub) space wi , influence coefficient of i-th factor (or i-th attribute). di , different between A and B events in i-th attribute. It is obvious that D(A, B) = D(B, A) D(A, A) = D(B, B) = 0

(9.6)

In addition, we can extend the measurement of difference in (hierarchy) space to uncertain events. For instance di (A, B) = μ A˜ (vi ) − μ B˜ (vi )

(9.7)

where μ A˜ (vi ) and μ B˜ (vi ) are the membership function of fuzzy events A˜ and B˜ with respect to its i-th attribute vi in the same (hierarchy) space.

9.3 Fuzzy Hierarchy Reasoning Approach

121

9.3 Fuzzy Hierarchy Reasoning Approach We now discuss the decision making process under uncertain environment. The decision is made taking consideration of several fuzzy attributes, which are both uncertain in quantity and its implicitness of relationship. If the decisive solution T (Fig. 9.1) is a particular state in its attribute space ψ, then according to (9.1) ψ is determined by its attribute state {ti } which can be further expressed by corresponding sub-attributes’ states in ljk . This situation holds true even in fuzzy representation. Thus, the process of decision making is transformed by a series of fuzzy state operation.

9.3.1 Space Chart Analysis Each attribute affected to decision solution T is presented in its own subspace ψi and is hierarchically through (9.2) influenced to the state of T in space ψ. The relation between ψ and ψi is shown in the space chart. The nodes of which represent the essential of the concerned factor sub spaces associated with the state of the attributes; Such as Fig. 9.2 shows the expression tree of candidate projects evaluation for NSF support, which clearly shows how the candidate project is evaluated by NSF evaluation rule for obtaining the financial supports from NSF.

Goal level

Ψ: Evaluation of candidate projects for NSF support

Criterion level

Indication level 1

Ψ1: Contribution to society Weight w1

Ψ’1: Practical meaningful Weight w’1

Ψ2: Competence cultivation Weight w2

Ψ’2: Scientific Ψ’3: Potential value Weight advantages Weight w’3 w’2

Ψ’4: Degree of difficulty Weight w’4

Ψ3: Project feasibility Weight w3

Ψ’5: Research duration Weight w’5

Ψ ’6: Budget Ψ Ψ support Weight w’6

Indication level 2 Ψ’’1: Economic Ψ’’2: Social revenue Weight w’’1

revenue Weight w’’2

Decision level D1: Encourage 120% fund

D2: Accept 100% fund

D3: Decline 80% fund

D4: Amalgamate D5: Postpone 30-70% fund

Fig. 9.2 Expression tree of candidate projects evaluation for NSF support

D6: Reject

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9 On Fuzzy Method of “Deep Knowledge” and “Deep Data” in Project …

9.3.2 Fuzzy State Assessment Fuzzy state assessment for each attribute in corresponding subspace with respect to its root attribute is performed by evaluation matrix through “pair comparison” based on empirical prediction directly related to fuzzy reasoning process. Define five grade scales for “pair comparison” of state:“1” represents that the state is slightly important to the system goal of decision; “2” represents moderate important; “3” represents obviously important; “4”, seriously important and “5”, extremely important. This 1–5 grade scales are feasible to quantize the conceptual thinking of professional experts to make judgment for evaluating the state status of attributes and formulating evaluation matrix. ( ) ( ) A = ati j = (ζli )/ ζl j

(9.8)

where i, j are the attributive subspace order of lower level attributes; t, l mean the abbreviations of top and lower. For instance, Fig. 9.2 is the expression tree of candidate projects evaluation for NSF support, ζli andζlj in (9.8) mean the membership function of i-th and j-th subspaces of lower level with respect to its root space. In the indication level 1 of Fig. 9.2, (a45 ) = (ζψ4 )/(ζψ5 ) = 3/1 = 3; It means that the importance of attribute 4 (degree of difficulty) and 5 (research duration) with respect to its root (project feasibility) is obvious important “3” and slightly important “1”.

9.3.3 Weight Distribution Between Attributes It is necessary to weight the attributes in the same level. Simulating this problem as sequencing physical weights of a set of body without a scale, we may get it if the total weight of body 1, 2, …, n as well as each of its weight ratio rij = wi /wj (i, j = 1, 2, …, n) are known. Suppose w1 , w2 , …, wn are the weight of body 1, 2, …, n respectively, then we can set up a weight ratio matrix A, where we have ( ) A = ai j Where: ai j = 1/ai j aii = a j j = 1 or

(9.9)

9.4 Illustrative Example

123

⎡w

1 w1 w1 w2 w2 w2 w1 w2

⎢ ⎢ A=⎢ ⎢ .. ⎣.

.. .

wn wn w1 w2

⎤ w1 ··· w n w2 ⎥ ··· w ⎥ n .. .. ⎥ ⎥ . . ⎦ n ··· w wn

(9.10)

Multiplying weight vector W AW = {nw1 , nw2 , . . . , . . . nwn }T = nw

(9.11)

(A − n I ) = 0

(9.12)

Equation (9.12) means the weight vector is the Eigen-value problem of “pair comparison” matrix A with respect to Eigen-value n, the Eigen-value reflects the weight distribution. i.e., the sequencing of weight distribution among attributes can be handled as an Eigen-value problem of (9.12).

9.4 Illustrative Example 9.4.1 Space Chart of Event We have defined the space chart of the event to be decided and have assessed by fuzzy approach the state of its affected attributes as well as its weight distribution with the root attribute. The fuzzy reasoning process can be carried out level by level. An example of evaluating candidate research projects for fund support is studied hereby and the factor chart corresponding to space chart is presented in Fig. 9.2, which is comprised of 5 levels. Goal level space also serves for the decisive solution T = Ti (i = 1, 2, …, 6). There are sub-spaces in each level as indicated in Fig. 9.2 and the attributes associated with its weight are also noted. Since the state of any attributes is described in its own subspace interacted with the others upon its root attribute in the root space, therefore, the evaluation matrix A of each root attributes actually reflect the heuristic prediction of human knowledge and constitute a part of fuzzy reasoning process. The evaluation matric Aψ i of each node in Fig. 9.2 with weight distribution are:

Aψ1 =

(

( ' )( ' ) w ψ1 ψ )(2 ' )i 1 3 (ψ1 ) 0.75 ' 0.33 1 ψ2 0.25

(9.13)

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9 On Fuzzy Method of “Deep Knowledge” and “Deep Data” in Project …

( ' )( ' )( ' ) ψ1 ψ2 ψ(3 w)i ⎞ ' 1 0.2 0.33 (ψ1 ) 0.10 Aψ1 = ⎝ ' 5 1 3 ⎠ (ψ2 ) 0.64 ' ψ3 0.26 3 0.33 1 ( ' )( ' )( ' )( ' ) ψ1 ψ2 ψ3 ψ w ⎛ ⎞(4 ' )i 1 1 3 2 (ψ1 ) 0.33 ' Aψ1 = ⎜ 1 3 2 ⎟ ⎜ 1 ⎟ (ψ2' ) 0.33 ⎝ 0.33 0.33 1 0.5 ⎠ ψ 0.10 ( 3' ) ψ4 0.24 0.5 0.5 2 1 ⎛

(9.14)

(9.15)

The weight sequencing for subspaces ψ’i ( ') ( ') ( ') ψ1 ψ2 ψ3 w = 0.43 w = 0.14 w = 0.43 1 2 3 ( ') 0.75 0.10 0.00 ψ ( 1' ) 0.25 0.64 0.00 (ψ2' ) 0.00 0.26 0.33 ψ 3 ( ') 0.00 0.00 0.33 ψ ( 4' ) 0.00 0.00 0.10 ψ ( 5' ) 0.00 0.00 0.24 ψ6

wi 0.34 0.20 0.18 0.14 0.04 0.10

(9.16)

The final decision can be achieved by fuzzy evaluation through fuzzy reasoning.

9.4.2 Fuzzy Decision Making The fuzzy decision making is based on evaluation of state of individual attribute in its own subspace ψ i . The global decision solution Di (i = 1, 2, …, n) in the decision space ψ. In Fig. 9.2, six alternatives are taken: D1 , encouraged project with 120% support funding; D2 , accepted project with 100% full funding; D3 , declined project with 80% support funding; D4 , amalgamated project with 30–70% supports; D5 , project postponed by certain reasons and D6, insufficient project being rejected. Membership function of individual attributes in ψ i ’subspace can be obtained by sequencing the weight distribution w’i with respect to Di (see Fig. 9.2). For which fuzzy evaluation matrix Aψ i (as (9.13) to (9.15)) should be formulated as ( ) Aψk =(ζik )/ ζ jk , ( ) ' ' ' ' ' ' i, j = D1, D2, D3, D4, D5, D6; k = w1 , w2 , w3 , w4 , w5 , w6

(9.17)

(ζik )/(ζjk ) represents the ratio of belonging of the solution Di and Dj with respect to k-th attribute in ψ k space.

9.5 Fuzzy Decision in Bidding

125

Substituting (9.17) to (9.12), a set of Eigen-vector can be obtained '

'

'

μk {μ1k , μ2k , . . . . . . , μ6k }, (k = w1 , w2 , . . . . . . , w6 )

(9.18)

Equation (9.18) is belonging vector of the attribute k to the decision solution Di, which exactly is the membership function of k to Di. i.e., μk˜ (Di ) =

μ2k μ6k μ1k + + ··· + D1 D2 D6

(9.19)

The fuzzy decision should be made by Dm satisfying ( 6 ) ∑ μm = Max. μik , (i = 1, 2, . . . , 6; 1 ≤ m ≤ 6)

(9.20)

k=1 6 ∑

(μik ) = 1.0

(9.21)

k=1

where (9.20) means the maximum of summation of membership function of all attributes in ψ’k (k = 1, 2, …, 6) subspaces with respect to decisive solution Di; and (9.21) means the summation of membership function of attribute k in ψ’k subspace with respect to all decisive solution Di (i = 1, 2, …, 6) equals to unity Eqs. (9.12), (9.18), (9.19), (9.20) and (9.21) represent the essentials of fuzzy reasoning for decision (Lin, 2002; Rebentisch, 2017).

9.5 Fuzzy Decision in Bidding In the construction market, the clients are offering tender and calling for bid. In responding to the call the contractor will submit the bidding proposal for the project with quotation offer. As a matter of fact, the offering cost is a key issue in total bidding activity. Since the higher the cost quoted, the less competitive the proposal will be, on the contrary, the lower the cost offered, the more the risk of the project will face. One should as soon as possible to estimate the cost before the deadline date of the bid on the basis of available data from data base, in which the attributes and costs of existing buildings are stored. The involving of fuzzy reasoning to conventional DB represents a new era of incorporating artificial intelligence (AI) with DB technologies enabling further deepening the available data and knowledge. In other word, it implies a new scenario of intelligent DB concept, in which the deep data (or deep knowledge) can be drawn from the conventional DB by fuzzy inference machine. Such a situation certainly will cause deep data and deep knowledge searching, the background causal information is analyzed by Shaopei et al. (2011).

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9 On Fuzzy Method of “Deep Knowledge” and “Deep Data” in Project …

9.5.1 Mathematical Modeling Supposing there are n engineering project samples A1 , A2 , …, An , and factor attribute set G has m elements: G = {g1 , g2 , . . . , gm }

(9.22)

As the tall building is concerned, where: g1 , represents the type of the structure. g2 , represents the characteristics of structure. g3 , stories of the building. g4 , storey height. g5 , architectural combination. g6 , type of interior decoration. g7 , floor area in total. ·················· ∼

If Gi is the fuzzy subset of i-th building to the attribute set G gi1 gi2 gim + + ··· + G˜ i = g1 g2 gm

(9.23)

where gij is the j-th membership component of i-th building to subset G and sign “+” is by no mean of plus, but the meaning of “collection”. The fuzzy subset of building to be quoted A* is G* A G ∗A = g1∗ /g1 + g2∗ /g2 + · · · + gm∗ /gm

(9.24)

where g*j is the j-th membership component of A* to subset G. Now the question is focused on fuzzy estimation vector e*A of the attribute of the building to be quoted, which can be expressed by e∗A = M.λ[α1 E 1 + α2 E 2 (1 − α1 ) + α3 E 3 (1 − α1 )(1 − α2 ) +1/3(E 1 + E 2 + E 3 )(1 − α1 )(1 − α2 )(1 − α3 )]

(9.25)

where: M, the total floor areas λ, empirical coefficient of rectification, which influences to the base price of the quotation very much α1, α2, and α3, the top three similarity degree (as shown in Eq. (9.25) or the top nearest three mathematical distance (as shown in Eq. 9.5) among existing building samples α1, α2, … αn. According to the principle of priority, it will serve as the basis of latter estimation. We have α1 >= α2 >= α3 E1, E2 and E3 are the corresponding estimation vectors of building project related to α1, α2, and α3 respectively.

9.6 Case Studies

127

In practical calculation, λ is suggested as 0.9–1.1. actually, λ is a multi-factor attributed value and is fluctuated by the condition of individual contractor/firms, market price of material and equipment, the prescribed goal of bidding and site environmental conditions etc. usually, λ is determined by extensive statistical data processing and studies based on the available data of existing buildings, which serve as samples.

9.5.2 Solving Procedures The solving procedure can be illustrated as following: (1) In the first step, one should identify the necessary attributes which reflect the overall characteristics of the building to be quoted. (2) Determine the approximate membership function of the building to be quoted to the attribution fuzzy set by subjective empirical prediction of statistical data processing. The strategy is that, taking one or two existing buildings as sampling object, the membership function of which is considered as the normalized standard 1.0, then the other components could be estimated by subjective information. This procedure is going back-and-forth through judgment by (9.24) until a comparative fuzzy relation between expected and sampling buildings can be established as shown in Table 9.1, in which the necessary information about the fuzzy set of the expected building is included. ˜ (3) Based on Table 9.1, the similarity degree of two fuzzy set in the universe X, A and B˜ can be expressed by ˜ ˜ B) ˜ = 1 [ A˜ ⊗ B˜ + (1 − A˜ ʘ B)] (9.26) ( A, 2 > > > > where A˜ ⊗ B˜ = [μ A˜ (x) μ B˜ (x)] and A˜ ʘ B˜ = [μ A˜ (x) μ B˜ (x)] x∈X

x∈X

˜ and A. ˜ Select represent the interior and exterior products of the fuzzy sets A the top three similarity degree under consideration with the corresponding estimations E1, E2, and E3. (4) The base price for quotation in bidding proposal can be estimated through Eq. (9.25).

9.6 Case Studies Case 1: The quotation of a reinforced concrete framework building for bidding. (1) The building characteristic attribution set. G = {g1 , g2 , g3 , ….., g8 }.

0.6

0.8

FCS SPF

FCS SPF & SCS

FCS SPF

Existing building C3

Existing building C4

Estimated building A*

PFP

RCRF

ICF LGF

ICF

PFP Deep foundation

0.5

0.5

0.8

1.0

0.6

CSM

CSM

Stone Plate

LimeStone Mount -ing

CSM

Descriptions

0.7

0.55

1.0

0.5

0.5

Reltn coeff

Exterior decoration

Reltn coeff 0.75

0.5

1.0

0.55

0.7

Descriptions

NS = 8 HS = 3.6 m NS = 12 HS = 2.7 m

NS = 7 HS = 3.6 m Side NS = 10 Mid. NS = 13 HS = 3.3 m NS = 10 HS = 3.6 m

Storey number storey height

ICC

IECC

CAL

File & document warehouse

ECC

Descriptions

Architectural combination

0.9

0.8

1.0

0.3

0.5

Reltn coeff

FAB

FAB

FAB & FCB

FAB

FAB & FCB

Descriptions

Floor material

1.0

1.0

0.8

1.0

0.7

Reltn coeff

4420.8m2

16200m2

9856m2

12045m2

4293m2

Descriptions

Total Floor Areas

0.9

0.3

0.3

0.4

1.0

Reltn coeff

To be determined

198.0

244.43

223.07

294.21

(RMB Yuan’s) Per m2

Total structural cost

A* is the fuzzy set of expected building with characteristic attribution set G. FAB—Floor of Artificial Brick, FCB—Floor of Ceramic Brick, IECC—Inner/Exterior Corridor Compartment, NS—Number of Storey, HS—Height of Storey, ECC—Exterior Corridor Compartment, FCS—Framework Casting on Site, SCS—Slabs Casting on Site, SPF—Slabs Pre-Fabricated, ICF—Independent Column Foundation, PFP—Pre-Fabricated Pile, IC—Inner Corridor, RCRF—Reinforced Concrete Raft Foundation, EC—Exterior Corridor, LGF—Local Grillage Foundation, CSM—Ceramic Surface Mounting, CAL—Classroom and Laboratory, ICC—Inner Corridor Compartment.

1.0

1.0

FCS SPF

Existing building C2

0.9

FCS SPF Semi-basement

Reltn coeff

Descriptions

Descriptions

Reltn coeff

Foundation situation

Structural characteristics

Existing building C1

Items

Table 9.1 Existing engineering building attributive data and membership relation coefficients

128 9 On Fuzzy Method of “Deep Knowledge” and “Deep Data” in Project …

9.6 Case Studies

129

where g1 represents structural type; g2 , represents foundation type; g3 , represents the condition of exterior decoration; g4 , represents the number of storey; g5 , represents the height of storey; g6 , the situation of architectural combination; g7 , represents the material of floor; g8 , represents the total floor areas. (2) Select the basic component element in fuzzy set as a calibration standard, assigning the membership function as 1.0, then establish fuzzy relationship by trial-and-error method shown in Table 9.1. Define the attributes of fuzzy set of sampling building as above and then the membership function of sampling building with respect to G is sequentially assigned in Table 9.1. (3) The fuzzy subset of sampling building to G are: ∼

G1 =

(

) ( ) ( ) ( ) ( ) ( ) ( ) 0.6 0.5 0.75 0.5 0.5 0.7 1.0 + + + + + + g2 g3 g4 g5 g6 g7 g18 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ∼ 1.0 1.0 1.0 0.5 0.3 1.0 0.4 G2 = + + + + + + g1 g2 g3 g4 g5 g6 g7 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ∼ 0.6 0.8 0.8 1.0 1.0 0.8 0.3 G3 = + + + + + + g1 g2 g3 g4 g5 g6 g7 ) ( ) ( ) ( ) ( ) ( ) ( ) ( ∼ 0.5 0.5 0.55 0.8 1.0 0.3 0.8 + + + + + + G4 = g1 g2 g3 g4 g5 g6 g7 0.9 g1

)

(

+

...................................................... The fuzzy set of expected building with characteristic attribution set G is: A* = (1.0/g1 ) + (0.7/g2 ) + (0.5/g3 ) + (0.7/g4 ) + (0.9/g5 ) + (1.0/g6 ) + (0.9/g7 ). (4) Define the similarity degree of A˜ ∗ to C1 , C2 , C3 and C4 , we take the similarity degree of A∗ and C1 first: A˜ ∗ ⊗ C1 = (1.0

>

0.9)

>

(0.7

>

0.6)

> > > > > > > > > > (0.5 0.5) (0.7 0.75) (0.9 0.5) (1.0 0.7) (0.9 1.0)

= . . . = 0.9 A˜ ∗ ⊗ C = (1.0

>

0.9)

>

(0.7

>

0.6)

> > > > > > > > > > (0.5 0.5) (0.7 0.75) (0.9 0.5) (1.0 0.7) (0.9 1.0)

= . . . = 0.5

The similarity degree 1 ( A˜ ∗ , C1 ) = (0.9 + (1 − 0.5)) = 0.7 2

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9 On Fuzzy Method of “Deep Knowledge” and “Deep Data” in Project …

Similarly. ( A˜ ∗ , C2 ) = 0.65; ( A˜ ∗ , C3 ) = 0.55; ( A˜ ∗ , C4 ) = 0.75 …………………………………….. (5) As mentioned above, take the top three similarity degree as α1 (=0.75) > α2 (=0.7) > α3 (=0.65) and the corresponding cost E1 = 198; E2 = 294.21 and E3 = 223.07 (RMB per square meter floor area). (6) The cost of expected building is Eq. (9.25), the. e ∗ A = M.λ[α1 E1 + α2 E2 (1 − α1 ) + α3 E3 (1 − α1 ) (1 − α2 ) + 1/3(E1 + E2 + E3 ) (1 − α1 ) (1 − α2 ) (1 − α3 )]. = 4420.8 × 1.0 × [0.75 × 198 + 0.7 × 294.21 × (1 − 0.75) + 0.65 × 233.07 × (1 − 0.75) × (1 − 0.7) + 1/3 × (198 + 294.21 + 233 + 233.07) × (1 − 0.75) × (1 − 0.7) × (1 − 0.65)] = . . . .. = 217.06 × 4420.8 = 959, 578.8 (RMB)

Practical finalized cost of expected building is provided latter on by contractor as 210 × 4420.8 = 928,368 (RMB). The error percentage is (959,578.8–928,368)/928,368 × 100% = 3.36% (smaller than 5%). Case 2: The quotation of a bridge based on fuzzy reasoning of attributes from data base of existing bridges The attributes of existing bridges ZSMRO, DXRB, ….. are listed in Table 9.2 with its unit price and costs for different individual parts. The fuzzy membership function of each structural component parts are respectively represented by the relational coefficients k as presented in Table 9.1 for different items of the structural characteristics. In our analysis, k is regarded as the ratio of the cost of individual structural part with respect to the “standardized” cost of the same rank structural part, since the membership relation can also be evaluated by the comparative cost of the bridge to be examined with the “standardized” one. In addition, k is manipulated for different structural component part, which will further improve the accuracy of fuzzy estimation. Table 9.2 presents the cost data of reinforced concrete bridges with the amount of concrete (M3 ), same operation as the case 1 is carried out for expected bridge ERCB. In 9-th row of Table 9.2 it shows that ERCB is quoted as 1345.3, yet the real cost is 1407.7, the error rate is. (1407.7–1345.3)/1345.3 = 0.046 or 4.6%. (smaller than 5%).

9.7 Conclusive Remarks Obviously, nowadays the problem that the conventional Data Base (DB) and Knowledge Base (KB) technologies are not satisfied to practical decision support. The decision maker used to have puzzled with the “shallow” data provided by conventional decision supporting system (DSS), which cannot be fully augmented in the decision

ZSMRD

1

SYJRB

JGRB

ERCB

7

8

9

825.9

KJRB

JTRB

5

6

2285.5

TDRB

4

1456.0

944.0

2790.0

563.5

2092.1

DXRB

XQRB

2

3

1406.6

Notation of Unit price bridge (RMB/m3 )

No

21,584.0

1584.0

3161.80

642.0

11,012.0

2115.0

12,505.0

17,270.0

Total amount (M3)

444.0

480.5

330.4

976.5

300.0

754.2

186.0

690.4

239.0

0.46

0.49

0.34

1.0

0.31

0.77

0.19

0.71

0.48

228.7

247.5

141.6

418.50

123.9

388.5

95.8

355.0

239.0

Pier Cost (RMB)

Foundation Cost (RMB)

Reltn coeff

Table 9.2 Existing R.C. bridge attributives data and membership relation coefficients

0.55

0.59

0.34

1.0

0.30

0.93

0.23

0.85

0.57

Reltn coeff

578.4

626.1

396.5

1143.90

349.0

982.8

242.3

899.6

604.8

0.51

0.55

0.35

1.0

0.31

0.86

0.21

0.79

0.53

Reltn coeff

Deck grillage Cost (RMB)

Deck plates

94.2

101.9

75.5

251.1

53.0

160.0

39.4

146.0

98.50

Cost (RMB)

0.38

0.41

0.30

1.0

0.21

0.64

0.16

0.58

0.39

Reltn coeff

Total cost 1345.3

Notes

9.7 Conclusive Remarks 131

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9 On Fuzzy Method of “Deep Knowledge” and “Deep Data” in Project …

process. Thus the development of intelligent technologies can able to provide “Deep Knowledge” and “Deep Data” seems necessary. From unified fuzzy set approach, this chapter deals with the reasoning and decision processes which are represented by fuzzy nearness degree and fuzzy similarity degree in a decision state space. Some developments in this regard are presented based on hierarchical sub spaces of state parameters of the decision event. An example of decision making for bidding submitted by project contractor and supported by an intelligent data base is presented for the purpose of illustrating the techniques of this chapter, in which the intelligent DB is inferred by fuzzy approach. It is an ever challenge due to the development of science and technology and competitive environment of decision making, a system which can provide data, information as well as knowledge in an efficient way (thorough-reaching implicit information) is urgently needed. The gap between what is expected in real world decision making is widening by the fact that there is a misunderstanding which leads the computer science people pay their attention in extending a DB system capability of manipulating data forms and organizations explicitly rather than in improving its quality. It seems the time for rectifying the tendency and common efforts should be paid by inter-disciplinary cooperation for improving these situation. All the illustration and derivation in this chapter are following to the PMI related guide (PMI, 2017a), as well as the PMI guide book for the project manager in its own professional development (PMI, 2017b).

References Lin, S. (2002). Fuzzy-AI in design consideration. Harvard University. Lin, S., & Huang, D. (2019). Project management under internet era. Springer Press & SJTU Press. PMI. (2017a). PMBOK ® a guide to the sixth edition of project management body of knowledge (6th ed.). PMI Press. PMI (2017b) Project manager competency development framework (3rd ed.). PMI Press. Rebentisch, E. (2017). (Editor in Chief): Integrating program management and system engineering. Wiley. Saaty, T. L. (1980). The analytic hierarchy process. McGraw Hill, Inc. Shaopei, L., Xiuling, G., & Yufei, W. (2011). A conceptual approach of fuzzy decision for systems by “deep knowledge” and “deep Data”. In Proceedings of the second international conference on the application of artificial intelligence to civil and structural engineering, London, UK.

Chapter 10

Perspectives in Combining Fuzzy and AI Techniques in Quantitative Management

10.1 Background Information 10.1.1 Facing Digitization of Project Management As the progress of science and technology, we are entering to the Internet digital era. The new generation of project management will be implemented on the Internet platform, where the layout, planning, implementation, tools, control and operation will be carried out through system analysis and structured implement methodologies. The essentials of traditional project management are to produce and transfer a series of decision through hierarchical framework of “organization” on the real platform for the implementation of project. Nevertheless, the subversive impact of Internet has seriously changed the ecology of project management. Under VUCA (Volatility, Uncertainty, Complexity and Ambiguity) era nowadays, the strictly theoretical based “Project Management” is no longer sufficient, it needs to be supplemented by “Project Governance” based on softening skills with the principles of “Softening of Theorem” and “Hardening of Knowledge” (Lin, 2008). As a matter of fact, “Organization” is just a real tool for transferring information, however, on Internet platform, it is possible to share, reproduce and transfer information top down and bottom up to each professional sector in real time basis, and realize the “seamless and high efficiency operations” of all the procedural items as an “organization” of project management during its implementation.

10.1.2 Case Guide On January of 2019, the directorial board of Automobile Company A is decided to manufacturing one million cars in the year of 2020, the decision is offered to CEO Mr. © Springer Nature Singapore Pte Ltd. and Shanghai Jiao Tong University Press, Shanghai 2023 S. Lin and G. Zhao, Fuzzy Quantitative Management, Fuzzy Management Methods, https://doi.org/10.1007/978-981-10-7688-6_10

133

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B, then, how Mr. B realized his mission through the organizational behavior under Internet condition? The organizational chart is hierarchically performed by different nodes in each level, then the interactions between nodes in each level for transferring information top down and bottom up, then, to realize the overall management to the mission of Company A in 2010. The CEO Mr. B has five Vice CEOs: C1, C2, C3, C4 and C5, responsible in procurement, production, technology, financial and supporting respectively, Mr. B operates the mission of Company A in 2020 through coordinating these Vice CEOs in offering different information and accepts their feedback information on the Internet platform in real time base, thus controlling and implementing the tasks of project management in 2020. Let’s take how Mr. B contacts with Vice CEO C1 as an example, B offers C1 the procurement tasks of 2020 for preparing all the materials and facilities of outsourcing. Since Vice CEO C1 has his/her department directors C11, C12, C13, C14 …., responsible to the purchasing tires, steel plates, engine, electronic facilities,…. respectively; Then C1 reproduces information and offers C11 to purchase 5.2 million of tires (each car will equip 5 tires including one backup and other 200 thousands tires are spare tires); offers C12 to purchase 520 thousands of thin steel plates (0.5 ton thin plate for each car); offers C13 to purchase 1.02 million of engines (20 thousands engines for backup) and offers C14 to outsourcing 1.01 electronic and communication facilities (10 thousands for backup), …. Let’s further take department director C11 as an example, his/her task is purchasing 5.2 million of tires, so C11 needs to operate with bidding and tendering process, he/she may further mobilize his/her subordinates section head C111, C112, C113,…for publicizing call for bidding, for supplier selection, for tender document evaluation and for procurement contracting,….. C11 also needs to feedback information of when and how much the budget required, of when and how big does the warehouse space required as well as of when will the transportation required by the purchase of tires to the Vice CEO C2, C4 and C5 respectively. The nodes at different levels hierarchically in the organizational chart on the Internet platform represent physically different people in each level of this chart; their function is just accept information, reproduce information and transferring these information top down and bottom up in real time. At the node of the chart, the reproduction of conventional information can be realized by defined software from regular professional procedures; the reproduction of information with human decision making can also realized by prescribed artificial intelligence software blocked in particular posts of the organizational network chart. The same situation will be happened in different nodes throughout the organizational chart. Therefore, the realization of organizational behavior of project management under knowledge economy and digital Internet era can be realized totally and automatically by the information transfer on the Internet platform, which may offer subversive impact to the traditional mode of project management under industrial economy era (Lin, 2018).

10.2 Two Types of Economy and Its Characteristics

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10.1.3 What Should We Understand? What should we understand project management under Internet + AI Era and knowledge Economy? It can be illustrated as following points: • Understand different mission of enterprise under industrial economy and under knowledge economy • Understand how a successful enterprise maintains sustainable development under knowledge economy? • Understand the program of studies for project management under digital era and knowledge economy. • Understand how digital economy is coming with practical examples. • Understand how AI and expert system work for engineering design purpose? • Understand how an intelligent engineering system works?

10.2 Two Types of Economy and Its Characteristics The industrial economy was started since the industrial revolution took place in western Europe since 18-th century. The characteristics of industrial economy is based on implementing the circulation of social production principle “CapitalProduction-Market”, where the enterprise possesses real capital (cash, bond, or other kinds of hard properties), by which the man power, materials, machine tools and technologies can be available, then the product manufacturing follows up for circulating the product in the market for profits. The repeated process of social production principle forms the basic rule in industrial economy society. For steadily carrying out this social production principle, the enterprise needs to find their appropriate products and to form all the production processes as a “project”. Through well organize all of these production resources and procedures, the “project” can thus be implemented. A series of strict management rules are necessary for the enterprise under industrial economy for carrying out this operation, by which the general principles of traditional project management under industrial economy was founded. The typical representation of industrial economy in reality was the Taylor’s production line in early twentieth century from the car manufacturing. Taylor’s production line has gathering all the resources with various regulations and rules along the line and shaping the basic principles of project management, which represents the productivity of the enterprise under that era. Though the success of an enterprise is not only depends on the project management itself, however, the assessment of project status will be in great extent depends on it. There are many attributes for establishing an enterprise: Firstly, one needs to have resources (capital, human resource, machine tools, materials etc.); secondly, well organize these resources and transfer it into productivity; thirdly, one needs to identify what does the enterprise doing? What are its mission, value and expected contribution to the society? Actually, project management can play a role as to

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bridge up the success of the enterprise with all of those attributes. As a matter of fact, every enterprise activity can be regarded as a project activity under defined concepts, theorem, methodologies, procedures and tools. In other word, project management is the very basis of enterprise vitality and insurance of its market success. The knowledge economy was emerged in late twentieth century in the United States due to the development of science and technology, especially the Internet and information technology. The characteristics of knowledge economy can be concluded that, not only real product but also virtual service product occupies the majority of social production and contribution to the GDP of a country. Where, the production is no longer operated on the real platform with real capital and real resources, but on the virtual platform with virtual assets (knowledge) and virtual means of resources – the creativity, innovative ideas and competitiveness of the products in the market. The mission of enterprise under knowledge economy is to mobilize its staffs for the transformation of their virtual knowledge assets into creative and innovative ideas for making their real and virtual products to be competitive in the market for earning the profits. The characteristics of knowledge economy is based on the circulation of social production principle—“Knowledge—Creativity—Innovative ideas— Competitive product—Market sale”, There will be no longer strict rule and enforcement management framework, the working rule can be rather flexible (Jing Dong Group Co., 2017). Based on information, management means to make a series of decision under uncertain environment. In the past, information was shared in a real platform yet not in a real time base, it always delay during its transferring and processing between different departments of the enterprise, which cause less efficiency of the management. Under the knowledge economy and Internet environment, every management related information is distributed and transferred on the Internet platform in the real time basis, which can be accessible by different departments simultaneously. Based on mass cloud memory, big data processing and AI reasoning, managerial data and information can be fully utilized to create powerful productivity, thus greatly increasing the efficiency in project implementation. Under industrial economy, each enterprise activity can be regarded as a project activity under defined concepts, procedures and tools. However, in knowledge economy, due to the enterprise possesses virtual knowledge assets and to process project through creative and innovative ideas for real and virtual products, the working platform and operating objects are different from previous one in industrial economy, the operation concepts, methodologies, procedures and tools in project management under digital knowledge era will have subversive changes than before, one needs to accommodating such situation and taking corresponding updated measures. It is worth to point out that even though the project environment has dramatically changed, but the basic principles and theorem of project management remain unchangeable, project management principles remains the very basis of vitality and insurance of market success of the enterprise under the digital Internet era.

10.3 Business Mode Under “Internet” Era and Knowledge Economy

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10.3 Business Mode Under “Internet” Era and Knowledge Economy It is recognized that the PMO (Project Management Office) under knowledge economy and “Internet” era is widely spreading in different professional industries. There is an irreversible trend that Internet will be used as a fundamental information platform for project management practice in each field. What should we do for pushing forward the project management profession under the “Internet” era? Doubtlessly, the business modes as well as the organization governance, either in project, program level or in portfolio level will be consequently changed, one cannot underestimate the impact of “Internet” environment to the former way of project management under industrial economy; this is why as the project management strategist, PMO needs to take corresponding measures against it as shown in later Sect. 10.9. The economic development has undertaking the path characterized from “market driven” to “requirement-driven”. The position change of individuals under “Internet” era is quite obvious; their career will change to the path of “knowledge–innovation –independence -personality-vision”. For their social position will be changed from “Company-staff” to “Platform-individual”, one may extend his/her ability to the extreme on the common social Internet platform and transform one’s working rule from “passive” to “active”, or from fixing in definite organization to facing the whole society for one’s contribution. One may rely on his/her social creditability, which is derived by social big data through Internet, and following to the path of “behaviorcompetence-creditability-personality-wealth” for obtaining his/her personal wealth and benefits. The relationship between people under “Internet” era will be no longer through “networking”, but through definite “rules”. The business rules are diversified upon individual client’s need along the path of “creativity—representation— displaying—ordering—design—production—clients”. Under “Internet” era social believing will be evolved along the path of “rule-order-morality-believing”, then the thinking philosophy and action of people will be greatly restrained by the Internet platform and Internet environment. The social industrial structure under “Internet” era is going to change from traditional one to Internet-based one. Moreover, the artificial intelligence technology will be further applied in the operations of different management stages for instant decision making by big data statistical analyses, machine learning and intelligent fuzzy reasoning etc., the latter one represents “Softening of Theorem” under digital era for dealing with uncertainties in real world. Thus, involving cloud calculation and Internet of things, it forms a comprehensive solving framework of “Internet + AI” platform, which may heavily destroy existing business mode. Furthermore, there will be the evolution of “Internet-Movable Internet-Internet of Things” to form a comprehensive platform of Internet ecology for promoting the productivity of the society. Certainly, it will deeply influence to the managerial philosophy and methods during the treatment of information in project management.

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As a matter of fact, management processes can be understood as a series of decision making, in other word, a process of maneuvering information and creating new information, the only difference is that in industrial economy era, such information processing and creating were performed on a real platform, but in knowledge economy era, it is on a unified “Internet” platform. Such as the organization in real platform can be represented by headquarter, different departments and its subordinate sections hierarchically to perform different information processing capabilities. However, on “Internet” platform, the same function of information processing can be also consummated and perform the organization function by sorting, classifying, distributing, districting, leveling and re-generating of information between different levels for performing the organizational roles. Entering to “Internet” era, the subversion to project management ecology and environment will heavily influence to the methodology of solution frameworks of project management, program management as well as portfolio management. For “Project Management”, all the operation procedures will be completed through internet, the database (including cloud base) should be built to be real-time accessible to all procedures during the life cycle of the project, the real time transferring of information between different departments makes it possible to accelerate the processes and thus rise efficiency of project management works. For “Program Management” under “Internet” platform, it is possible to accessing millions of transactions (projects) in parallel with the strictness of reviewing, monitoring, controlling and updating rather than conventional “program management” to handle just at most dozens of projects. The management philosophy and the method of maneuvering information and data for numerous projects from unified (or distributed) data base (or cloud data base) have totally changed. For “portfolio management” under “Internet” platform, it is also possible to access and compare numerous initiatives under strict criteria of selection by specified requirements, which is impossible to realize in the past. All above mentioned statements show that the emergence of “Internet” platform provides us the possibility to carry out comprehensive best practice and high efficiency of project management. Nevertheless, the modes and procedures of project management under “Internet” era have been subverted; the corresponding project management mode is subjected to big challenges and needs to be rectified. Doubtlessly, under digital knowledge economy, the main asset of an enterprise is no longer real one, but virtual knowledge. An innovative enterprise is based on creativity, it also needs the competence of transferring creativity into product competitiveness in the market; correspondingly, the critical mission of an enterprise under knowledge economy nowadays is how can they transform their virtual asset “knowledge” to innovative ideas and finally become the market competitive real and virtual products serve for their clients. Therefore, for dealing with knowledge assets under digital era, all the knowledge and experiences of the enterprise should be carefully preserved in long term, which represents “Hardening of Experiences”. The future enterprise will become a knowledge-based organization with the potentiality of being an innovation initiator and an innovation idea transferor. The emergence of “Block Chain” implies a revolution in project management under Internet environment, which is based on distributive accounting network

10.4 Sustainable Development of Successful Enterprise …

139

concept. As the development of digital economy and with weakening of organizational function, the decentralized network structure will make the global organizational risk approaches to minimum; in the meantime, the mutual coordination by each block and the function of individuals will be strengthening as well. Consequently, under “Internet” era, the subversion of professional chain seems to be happened from the chain of “producer-agent-consumer” to the chain of “consumerdesigner -producer”. Agent in the past business chain will be replaced by designer for satisfying numerous customized expectations and requirements. Certainly, the specifications and standards of project management throughout the world need to update and modify in its contents accordingly. As “Internet” era is inevitably approaching to our real life and causing dramatic changes, based on successful best practice of numerous projects, we need to search the essentials and new tendency of project management under “Internet” era and trying to develop a new standard or practice guide for accommodating the tendency of development. Through examining of some Internet-based enterprises, it is recognized that the necessity of promoting this new approach of project management and guiding it for its further healthy development, we suggest that further investigation along this direction in totally “digitization” transformation of whole profession should be promoted. It needs to establish corresponding transformation infrastructures, necessary R&D works as well as follow-up to solve practical problems during the specific professional transformation. In which the joint efforts of task force team from related enterprises and institutions are needed for deepening the studies of the problem.

10.4 Sustainable Development of Successful Enterprise Under Knowledge Economy If you are a successful enterprise today, but you are not necessarily to be the successful enterprise tomorrow! A lot of practical examples show the truth that the vitality of an enterprise is deeply planting in its technical and business potentiality of accommodating to future market development. Under knowledge economy and VUCA era, market reveals much changeable, based on market information, the enterprise needs to use its knowledge assets for taking innovative strategies and reform approaches for covering the inconsistency from market needs. The rule of “knowledge—innovative ideas—new market strategy—core competitiveness—new products—new markets” will rescue the enterprise from the market risks and continuously to maintain its sustainability. Once the enterprise insists its traditional operation mode, which violets to the market needs, then failure is inevitably to occur. Let us take the experiences of how Zhen-Hua Heavy Industry, Ltd. (Co & Ltd. xxxx), a successful machine-manufacturing enterprise, characterized in producing heavy mechanical installations have taken re-consideration for her sustainable development after subversive market changes. Its experiences in successful operation and maintaining sustainable development by re-orienting to a group of new products

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under market changing, have widely attracting eyes from the professional community the world-wide. In 90 s of last century, the development of Zhen-Hua enterprise approaches to extreme, while: (1) The total capitals and fixed assets including production and transportation installations have increased dramatically to be doubled within two years; (2) The production sale increases 30% per year, and the world market occupation of its main product container crane approaches 70% more; (3) Organizationally, the staff number, and their technical level have insistently increasing; (4) Diversifying their products for covering different kinds of heavy machinery, extending their available markets, also exploring new markets; (5) The enterprise has greatly increasing its capability in resistance of risks, the “Whole Business Operation Chain” including design, production, manufacture, sale, transportation, assembly, testing and after sale service, has established; (6) Greatly increase market reputation of the enterprise and its trade-mark. The successful development of the enterprise is due to external favorable conditions, such as the expansion of world market needs in container crane. As regard to internal factors, the successful development of the enterprise was caused by strong market competitiveness due to: (1) Focus on providing container’s crane all over the world, for accommodating the world economic growth (Fig. 10.1); (2) Low product cost based on low price of labor and land; (3) Short delivery period is due to standardization of design procedure, powerful manufacturing capacity, core competitiveness technology of group shipping of

Fig. 10.1 Providing products on container’s crane

10.4 Sustainable Development of Successful Enterprise …

141

the crane without re-assembling on site, thus accelerate of capital circulation flow, and causing lower operational risks (Figs. 10.2, 10.3); (4) Reliable creditability in bank loan and other kinds of financing means, which guarantee the financial operation of the enterprise; (5) Pay serious attention on talent cultivation, promoting technical development and exploration of new products and new markets;

Fig. 10.2 Integrated group shipping

Fig. 10.3 Group shipping vessel crossing the golden gate bridge, USA

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(6) Sum up experiences and draw lessons from enterprise practices. Strengthening exchange and cooperation between enterprises at home and abroad; (7) Advocate enterprise culture and spirit of cohesiveness for the enterprise. Nevertheless, as to world-wide economic depression in 1998, international navigation logistics degraded correspondingly due to decrease of international trade, which induced to serious drop down in demand of new harbor construction and consequently container crane demand, all advantages of enterprise were inactivated, such as cost increasing, market saturation, rough material inflation as well as exchange rate variation, etc. It means that the sustainable development of the enterprise was facing tremendous challenges. How to rescue the situation and maintain prosperity? The enterprise must go through the principle of “knowledge-innovative ideas-new market strategy-core competitiveness-new products-new markets” to find the new way of sustainable development by exploring its new products and new markets. Sustainability under unfavorable environmental changes is an important subject facing to each enterprise; yet maintain sustainability is the only choice especially for large scale enterprises. Not only the revenue, profit and market occupation are the first prerequisites; schedule, cost and quality are the basic requirements in the second; clients’ satisfaction; minimum scope variation; bright corporation culture; smooth working procedures and value outlook are also have to be maintained. The sustainability thinking of the enterprise can be concluded as follows: Firstly, to keep core competitiveness in exploring new products for accommodating new market demands; secondly, integrating resources and services both from upper to lower streams of the product development chain; and thirdly, entering to international market and explore the international business through joint merging and acquisition with partners; lastly rely on technical innovation, the enterprise could totally re-structuring its business mode with innovative products. Followings are the details: (1) Targeting to offshore development for oil & gas exploration and production, keeping core competitiveness for exploring new products of: • • • • •

Steel structures of large scaled marine bridges (Fig. 10.4) Heavy marine floating cranes (7,500 – 10,000 t) (Fig. 10.5) Explore various offshore platforms (Fig. 10.6) Bulk Cargo Loading and Unloading Machine (Fig. 10.7) Harbor Loading and Unloading Machine (Fig. 10.8).

(2) Integrating resources and services both upward and downward in product development chain for providing EPC (Engineering-Procurement-Construction) mode service, the enterprise will not only play a role as supplier, but also responsible to scheme, planning, design and construction works in the “Turn Key” service. (3) Cooperated with another domestic harbor engineering corporation for carrying out merging & acquisition abroad or hold control share to well-known foreign engineering company, entering to international engineering market for exploring international business through EPC projects as a general contractor in planning,

10.4 Sustainable Development of Successful Enterprise …

Fig. 10.4 Explore New Product—Long-Span Steel Bay Bridge in California, USA

Fig. 10.5 Explore New Product—Heavy Marine Floating Crane

143

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Fig. 10.6 Explore new product—offshore platform

design and construction, also as a supplier in procurement, for obtain profits from different aspects. (4) Based on strong technical potentiality of the enterprise, it re-oriented its function as from product supplier to system supplier, from providing hardware product to intelligent product combined with hardware and software. Moreover, the enterprise was being responsible to the construction of an automatic/intelligent harbor. It is a symbol of new development that realizes the sustainability of the enterprise has crossed the stage of quantity increasing to a new stage of quality leap. The considerations of sustainability of Zhen-Hua Heavy Industry, Ltd. can be concluded as: Continuously exploring new products and new market by core competitiveness; integrating the upper stream and down-stream resources for EPC service and carrying M&A overseas for exploration in the international market. The symbols of a successful enterprise under knowledge economy are not just of those productivity, efficiency and revenue, which are necessary but not sufficient. A matured enterprise not only should hold a large percentage of market shares, but also needs to possess the potentiality of ever increasing her market occupation rate. Therefore, knowledge, intelligence and innovation are necessary conditions needed for maintaining sustainability of an enterprise in knowledge economy

10.5 Case Study—E-commerce and Logistics

145

Fig. 10.7 Bulk cargo loading and unloading machine

and digital era. It also means that there is still a long way to go for a successful and matured enterprise for continuously improving her capability in maintaining persistent sustainable development under the challenges in the digital era. We hope to aware those who are self-satisfactory nowadays by available enterprise achievements, to pay their attention on market changes and business situation for restructuring and updating their strategic planning, especially under such a fast growing in technical advances and subversive impact of knowledge and digital era. If one ignores and under-estimates these problems, such as Apple i-phone replaced conventional phone; digital photo replaced sensitivity photo etc., the enterprise would never be matured enough and would never approaching to its sustainable development.

10.5 Case Study—E-commerce and Logistics Technical progress is a driving force to improve our daily life, especially when we are entering to digital Internet era, it brings subversive changes in our life and changes social ecology and pushes business to operate from real platform to virtual one, and deal the objects from real to virtual as well. As regard to retail business,

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Fig. 10.8 Harbor loading and unloading machine

it is essential that tremendous improvement of the retail principle “Cost-efficiencycustomer satisfaction” can be achieved through Internet platform. An e-commerce and logistics delivery company Jingdong 618 (Jing Dong Group Co., 2017) in China can be used as an example to show how successful a retail enterprise could be established a complicated system, which takes advantages of artificial intelligence, cloud calculation, data mining, big data and statistics, to covering whole processes of procure, sale, dispatching and service of delivery to client. Besides, based on the individual preference of clients, there is a “recommended system” serving to provide clients the information of commodities and corresponding suggestions. In application of pattern recognition and voice recognition, the JIMI client service robot of Jingdong 618 is also a best practice, where the technologies of machine learning, neural-network, and natural language treatment have fully utilized for saving the cost of manpower as well as the to sustain the peak pressure of the business information treatment. The retail business system of Jingdong 618 is presented in Fig. 10.9, it comprises of input (PC, App, Mobile etc.), searching system, advertisement system, sales promotion system, order system, commodity supplier system, supply chain system, payment system and logistics system. All of those systems need to withstand the data flow

10.5 Case Study—E-commerce and Logistics

147

during peak pressure of their business, such as in October 1st national day of China, there will be several billions of deals in e-business ordering. Actually, there are four challenges in sales promotion system: Firstly, it is extreme mass data flow in the system net including some billions of commodities; secondly, there consists several dozen billions of information retrieval and transfer, which can easily collapse the system; thirdly, real time response of the price changing, any delay will cause complain from the clients; fourthly, quoted price isn’t a simple matter, one needs to calculate differentiation price from the others according to sales promotion rule. In 2017, Jingdong 618 combines functions of “Technique + Retail”, “Trade-Mark + Retail”, “IP + Retail”, “Finance + Retail” and “Multi-terminal + Retail”, linked with online and offline to promote the participation of consumers and trade-mark suppliers into common action, thus, transforms its role from an intelligent retailer to a retail infrastructure provider. The intelligent commodity sorting and searching system, dispatching and logistic system are another hot sport of Jingdong 618, in which there are compatible chain of business ordering system, man-less warehouse, automatic stereoscopic garage Fig. 10.10, auto-sorting system and intelligent logistic robots/shuttle vehicles to form a comprehensive auto-sorting and dispatching system, it strongly support the business success of the enterprise. Among the system, according to media report, supported by central sorting system, the auto-sorting system can deal with 71 thousand pieces of parcel per hour, replaced approximately 150 labor’s productivity per hour; autosorting process is performed by scanning the parcel when it arrives sorting machine, then deliver it to its destination. The intelligent logistic robots and shuttle vehicles as

Commodity System Stock System PC Pricing/Sales Promotion System App

Coupon System Order for Goods System

Mobile Settle Account System Others

Purchase Vehicle Platform Sending and Dispatching …………………

Fig. 10.9 Retail business system of Jingdong 618

Big Data Searching Machine Standing Book Operation

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Fig. 10.11 are shuttling back and forth in sorting and dispatching hall, which perform powerful productivity of the enterprise. The successful practice of Jingdong 618 has verified that technology is not only a tool for the enterprise, but also the innovative driven force in its business growth,

Fig. 10.10 Internal view of automatic stereoscopic garage

Fig. 10.11 Working of shuttled vehicle each loaded 150 kg

10.6 Program of Studies on Project Management …

149

client satisfaction as well as in creative sustainable development. It is obvious that Jingdong 618 is a technical enterprise, rely on technical progress to achieving business development. Through digital revolution, under “Internet + AI” ecology, it has been transformed from a traditional e-commerce company into an enterprise with intelligent e-commerce and logistics/delivery capabilities.

10.6 Program of Studies on Project Management Under Digital Era and Knowledge Economy We recognize that even though the emergence of digital Internet area will not totally changed the basic principles of project management, however, the methodology, operation procedures, platform and tools were changed. So we need further to study the new ecology of project management for its healthy development. Fortunately, a new generation of industrial talents, who are personally involved in the challenge of “Internet + AI” vortex; tightly related to his present job and professional responsibility in different fields is formed. The talents who are full of passion in innovation and discovery, and have had sound basis in IT and AI technologies, have growing up and widely spread in different fields of digital practices in social and industrial applications. They are enthusiastically involving to the movement of studying the changes of project management under digital Internet era through their practice and realized it is the great cause of development in modern project management. As to the next step, what we suggest to a researcher involved in the great topic of “PM under digital Internet era and knowledge economy” is that, for building the framework of our task, we may base on our present work, rectifying our understanding in this topic and following to the track below likely to 12 fundamental problems for establishing the theoretical and application system framework of modern project management in coming future: (1) (2) (3) (4)

Why the renovation of business system management under digital Internet is needed? Your understanding of digital Internet era in Knowledge Economy – Why it is a subverted impact to project management? What are the characteristics of project management under digital “Internet + AI” era? What are the competences and knowledge structures of a project manager needed in the coming future? What are the thinking philosophies (strategic and tactic) for a project management talent in the digital era?

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(5)

What are the characteristics of “PM Talent triangle” (including technical project management knowledge, commercial strategic analyses and leadership in each side) and its concrete contents under knowledge economy and “Internet + AI” era? (6) What are the differences of the rules defined in current PM knowledge system based on industrial economy and that of based on digital era and knowledge economy? Can you specify one by one in its “operation platform” and “dealing objects” changed from “real” to “virtual”? (7) Develop the project management procedures of e-business under digital Internet era and indicate the differences in managerial concepts, theorem, procedures, tools and operation with the former project management procedures; (8) Extending your understanding from (7) of the project management to ebusiness projects for program and portfolio management fields under digital “Internet + AI” era and in knowledge economy; (9) Identifying what the knowledge structure and competence of a project management talent under “Internet + AI” era are required? (10) What project management innovative measures should be taken for accommodating digital “Internet + AI” era? (11) Find out the differences in e-business project than before at: (i) Project life cycle; (ii) Scope, WBS, and SMART (specific, measurable, achievable, relevant, traceable) principle; (iii) Project personnel and organization; (iv) Project planning, scheduling, implementation and control (v) Program management; (vi) Portfolio management. (12) How to realize the totally “digitization transformation” of project management for organizationally systematic implementation to a multi-functional universal enterprise? What are the infrastructures of this transformation? What are the R&D works remain to carry out? And what will be the details of specific applications (such as in railway transportation, in telecommunication, etc.) of this transformation? Above mentioned is the program of studies in “PM under Digital Era and Knowledge Economy”. Based on practices under digital era and tracing along the predescribed way of studies, our goals can be certainly achieved by the collective efforts of our updated PM talents.

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10.7 Case Studies—Expert System for Airplane Structural Design 10.7.1 On AI and Expert System for Structural Design Based on AI technology, the knowledge base exert system can be effectively applied in engineering structural design. The design, manufacture, operation and testing of engineering structure need comprehensive knowledge, which have just partially reflected in codes and specifications, these knowledge depends on experience cumulated from the long-term practice by professional experts. As a matter of fact, the majority of such engineering experience is empirical, non-structured, which cannot be expressed by explicit analytic mathematical formulations, thus it causes the knowledge transfer during design a difficult problem. Engineering solution is a comprehensive and complex interactive process. Taking engineering design as an example, it is carried through conceptual initiation of object, preliminary design, detailed design, system modeling and optimization etc. It also needs multi-disciplinary knowledge and experience, while the AI, knowledge engineering will make it possible to form the framework of theoretical expression, explanation and treatment. When the knowledge is going to digitize and store into computer (hardening of knowledge) for applying in different situation of engineering design, it will play a big role for future application, as the expert system will also play the role in design by knowledge supports. It is worth to point out that expert system supported by knowledge engineering can be widely and effectively used in many fields, such as in chemical, medical, geological, meteorological, educational and military events in convincing diagnosis, explanation, decision making and treatment. It can be used for: (1) Serve to the client with authorized consultation knowledge; (2) Serve to the client with rich shareable knowledge base and data base as the supporting environment of scientific and comprehensive consultation; (3) Consulting service to the client’s interesting problems by means of inference mechanism for inferring new knowledge; (4) Provide explanation, evaluation and prediction of various social and engineering problems, so that people can reasonably allocate and handle resources. The core problem is the methodology in digitization of knowledge and building the knowledge base for subsequent problem solving. Figure 10.12 shows the knowledge digitization for knowledge capturing and knowledge base building, where digitization transformer DT can be realized in different approaches.

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Fig. 10.12 Knowledge Capturing for Knowledge Base Building

Initial Stage

Planning Execution Closure Stage Stage Stage

Gate

Gate

Gate

Gate

DT

DT

DT

DT

CAPTURING BEST PRACTICE INTO KNOWLEDGE BAASE

DT – Digitization Transformer

10.7.2 Production System and Inference Network The basis of solution system of many expert systems is to match the defined problem with the simple rules as we called “IF–THEN-ELSE” production rule. It is called “Rule-based system” for the known facts and the rules have been considered in software design in advance. Production system expresses expert knowledge by means of a set of rules, it has an expression as:

Use identifying structure as an example, the rule table of production system by LISP language can be expressed as the followings:

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The inference mechanism of expert system is expanded term by term by the knowledge rules from knowledge base, and make it every possible to expanding the identified facts. The inference process is carried out along inference network through “Forward Chaining” and “Backward Chaining” as shown in Fig. 10.13. In the inference network of Fig. 10.13, each node represents a fact or assert, connected by the arrow. The arrow is launched from one node to the other, such as in the figure that determination of aviation structure is ID1; determination of composite material structure is by ID2 and ID3 is for determining of aluminum alloy structure. The nodes in the bottom represent the original facts; the nodes at the top represent the assumed conclusion and the nodes in the middle are either the arrow launched out precondition, or the arrow ripping into for the conclusion. During inference process, the common used “Backward Chaining” method is a solving procedure starting from the un-vouched assumption and try to prove it by finding the rules that can verify the assumptions and also the facts that the rules can be applied; In the contrast, the “Forward Chaining” method is starting from a set of fact, followed by adding new facts and repeatedly trying to fit it with appropriate rules, terminated by utilizing all of the rules. Meanwhile, several new facts found by the solution process can be also used for stimulating other rules.

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Fig. 10.13 Inference network of structural cognition

10.7.3 The Building of Expert System for Airplane Structural Design It is well known that expert system is an important branch of artificial intelligence technology; expert system will certainly consist of an important constitutive part in the conceptual design of aviation structure. Let’s call back for the CAD of aviation structure, the ODIN (Glatt & Hague, 1975) (Optimal Design Integration) and AVID (Wilhite & Rehder, 1979) (Aerospace Vehicle Interactive Design) systems developed by Langley Research Center of NASA possess the functions of synthesis, feedback and optimization during the global integrated design processes. Starting from the mathematical modeling of exterior geometric configuration of the air-vehicle, the calculation of aero-dynamics loading, selection of propulsion system, flying feature calculation as well as ballistic calculation etc. will be carried out. The final solution is the optimum one through weight appraisal. Above-mentioned procedures are repeated by the help of interaction feature and database capability. Expert system will certainly involving expert experiences and realized the structural design by means of knowledge base, data base and inference mechanism of the system.

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The potentiality of application of artificial intelligence technology in the CAD is quite obvious, especially in the conceptual design of air vehicle structures; expert system has its better perspectives. Since: (1) The conceptual design is the critical step in the preliminary design of structures, where the knowledge and experience of expert are particularly needed, and it is difficult to obtain by pure computer software; (2) For the designer is difficult to interfere computer processing in design issues, therefore, the correctness of design cannot be checked by designer radically, rather than design expert system; (3) The design expert system is well development to perform every puzzling facing to the designer, it becomes an efficient tool of designer in design problem solving; (4) The design expert system is strengthened in conceptual knowledge rather than in mathematical formulations, which is accommodating to conceptual design and preliminary design stage The Fig. 10.14 shows the design expert system framework of aviation vehicle and the explanation of which can be listed as below: (1) VENPLN represents the sub-system Block-1 of geometric arrangement and planning; determine the general layout of the air vehicle, and calculate the main parts, such as the fuselage, wings and empennage by means of geometric software to determine the volume, area, rotational inertia, centriod and later aero-dynamic load calculation. The sub-system block-1 mainly for selecting optimum alternative of the aeronautic structure based on the rules specified by codes and experiences for satisfying the flying requirements of the airplane under the specific impulse and engine weight for the initial alternative. The Block-2 will automatically check the characteristics of flight parameters. In Fig. 7.14, the KBPRO, DBPRO and IMPRO represent k constraints of weight and size. In Fig. 7.14, the KBPLN, DBPLN and IMPLN represent knowledge base, data base and inference mechanism of planning respectively. (2) VENPRO represents subsystem Block-2 for the preliminary design propulsion system. It can determine the engine type, ratio of propulsion and initial weight during taking-off, so as to select propulsion knowledge base, data base and inference mechanism of propulsion design respectively. (3) VENSTR represents subsystem Block-3 for the optimum structural alternative in geometrical layout, structural solution in safety and economy. Upon which the total structural sizes, different part weights, center of gravity and rotational inertia can be determined. In Fig. 7.14, the KBSTR, DBSTR and IMSTR represent knowledge base, data base and inference mechanism of structural design respectively. (4) VENEQP represents subsystem Block-4 for the final optimum selection of equipment in the air vehicle; Based on design requirements and the cost and functions, design decision were made including mechanical parts, apparatus of communication and electric installation of the air vehicle. In Fig. 7.14, the

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KB

General Control Structure

DB

Consults & Suggestions Facts & Data

User1 User2 ……… User n

Block-1

Block-2

Block-3

Block-4

Block-5

VEHPLN

VENPRO

VENSTR

VENEQR

VENMNF

KBPRO

KBSTR

KBEQR

KBMNF

DBPRO

DBSTR

DBEQR

DBMNF

IMSTR

IMEQR

IMMNF

KBPLN

DBPLN IMPLN IMPRO

Graphic System

Graphic

File

Output

Output

Fig. 10.14 Design expert system framework for air vehicle

KBEQP, DBEQP and IMEQP represent knowledge base, data base and inference mechanism of equipment design respectively. (5) VENMNF represents subsystem Block-5 for the optimum manufacturing planning development, aimed to determine the optimum solution in quality and cost under required manufacturing duration. In Fig. 7.14, the KBMNF, DBMNF and IMMNF represent knowledge base, data base and inference mechanism of manufacturing design respectively. The inference procedure and corresponding knowledge flow and data flow are expressed in Fig. 10.14, which is a mega system model supporting to multiple users environment with sufficient software supporting . Thus the design of bearing struc-

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tural system of air vehicle can be carried out by using production expert system with the functional and material property information presented in structural design hierarchical tree as Fig. 10.15. Obviously, Fig. 10.15 represents a set of structural design solution, for determining the appropriate alternative, one need to travel the tree by means of “Heuristic Elimination Rules” assigned by the designer in advance. The node can be selected as feasible alternative only if it can pass all the requirements through the tree traveling. The processes are shown below: IF (DIAMETER OF VEHICLE IS GREATER THAN IOOOMM) AND (3D SYSTEM IS UNSTIFFENED SHELL STRUCTURE) THEN (ALTERNATIVE IS NOT FEASIBLE) IF (3D SYSTEM IS STIFFENED SKIN STRUCTURE) AND (2D SYSTEM IS DIAPHRAGM) THEN (ALTERNATIVE IS NOT FEASIBLE)

It is concluded that engineering design is related to a broad comprehensive knowledge of multiple disciplines; the arts of design cumulated during long-term practice will not be mastered by a minority of design experts, as the development of computer science and artificial intelligence, the simulation of design arts can be gradually explored for providing scientific argumentation for higher design quality, such as the expert system presented hereby.

Longitudinal System 2-D subsystem

AND

Transverse Framework Aluminum Alloy Structure

3-D stiffened structure AND

Transverse Diaphrama

Transverse Framework

Aluminum Alloy Structure

Composite Structure

Alternative-1

Transverse Diaphrama Composite Structure

Alternative-2

Shell Structure Aluminum Alloy Structure

Alternative-3

Fig. 10.15 Hierarchical information tree of aviation structural design

Stiffened Skin Structure

Composite Structure

Alternative-4

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10 Perspectives in Combining Fuzzy and AI Techniques in Quantitative … Independent analysis system

Independent management information

and graphic system

system MIS and decision auxiliary algorithm

Computer Aided Design System (CAD)

Decision

Supporting

System

(first generation)

(second generation )

Intelligent CAD (third generation )

Intelligent Design System (IDS)

(fourth generation)

Fig. 10.16 Four generations of intelligent design development

10.8 Internet + AI Based Engineering Application Systems 10.8.1 Background Information The technology explosion is one of the characteristics of our era, while Internet offers subversive impact to different fields of social life and essentially change the ecology of our life. Moreover, the widely application of artificial intelligence technology has further making the automatic solution and decision making possible, which also changes the management issues in real time accessing and covering a broader field. It is doubtless that “Internet + AI” will dominate our future social productivity and achieving technical and economic efficiencies. The most realistic transformation of application systems are updating available systems into an intelligent one, i.e.; to transform CAD into Intelligent CAD; similarly, to transform MIS into IMIS, DSS into IDSS etc. AI can be simply illustrated as the technique of simulating the intelligence of human being by means of computer, including knowledge based expert system, problem solving theorem, robot system, vision ability, voice recognition and natural language understanding. From application point of view, it can be subdivided into two categories: (1) The combination of AI with hardware—It includes different intelligent robot system, including different kinds of intelligent machine, intelligent instrument and various intelligent hardware; (2) The combination of AI with software—It includes ICAD, ICAM, IMIS and IDSS etc.

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We will focus on discussion the AI combined with software systems for engineering applications in design, planning and managerial decision making Fig. 10.16.

10.8.2 The AI Exploration for Application Systems On the other hand, the development of intelligent design appears its tendency of integration, such as multi-level, multi-aspect and multi-stage integration satisfying different design requirements. The system architecture of intelligent design system is shown in Fig. 10.17, where there exist multi-media interface, generalized integration model (mathematical integration, knowledge integration and network integration) and intelligent model (including self-adaption, self-learning and self- organization). There still performs heuristic-optimization approach in the system structure, combining AI inference mechanism with operational research and theorem of optimization. Another function of the system is that it equips with the feature of multibase synergistic work supported by DB, KB, model base and method base served for design purposes.

10.9 PMO Under Internet Era It is recognized that the PMO under Internet has been widely spread over different professional industries. There is an irreversible trend that Internet platform has become as a fundamental tool for project management practice; correspondingly, the philosophy of PMO operation must be subjected to changes. What should we do for pushing forward the project management profession under Internet era? It is a realistic challenge problem facing to all of us that we need to take following situations into consideration for determining our PMO strategy: (1) The Government Impetus of Promotion Many countries are stepping in improving government services via the Internet. It aims to set up a nationwide Internet-based government service system and using Internet tools to facilitate public services, which is an important step for accelerating the processes, where Internet as the fastest and most convenient way for the government to interact and provide service to the people. Accordingly, all the industries should be subordinating and being accessible to the nationwide government Internet platform for their normal business operation, it also inter-connects to government agencies and other business partners. Thus, the operation mode of Internet era for project management will be inevitably blossom, since the business modes as well as the organizational governance, either in project, program level or in portfolio level will be consequently changed. One cannot underestimate

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Man-Machine Intelligent Interface

Multi-Media

Generalized Application Model

Integrated Model

Intelligent Model

Output of design Self-learning

Self-adaptive

Self-organizational

documents

Heuristic optimization

and

design drawings. Multi-base coordination Intelligent server

optimizer

Evaluator

Analyzer

Solver

Data base

Knowledge base Model base

Method base

Graphic base

Inference machine

Fig. 10.17 The system architecture of intelligent design system

the impact of Internet environment to the traditional way of project management; this is why we need to take corresponding measures against it for the best practice toward success. (2) Necessity of Internet from Social Economic Development The law of economic growth has undertaking the path from single different individual countries to “globalization economy” driven by multi-element market requirements. In the past the production and distribution of social resource are decided by individual market, due to dis-connectivity of market needs, the residual productivity and overlapped production have emerged in many industrial sectors, which have been caused serious losses. Internet, especially the platform for the global market can eliminate those phenomena of over-construction or uneven investment that has been happened in past years. So, as to save social resource and avoiding waste by those duplicate and residual productivities, it is necessary to optimizing social resource distribution through Internet for the healthy economic development in all over the world.

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(3) Characteristics of Individual under Internet Era The change of individual under “Internet Plus” era is quite obvious, the career will along the path of “knowledge-innovation-independence- personality and vision”. For the basic social structure will be changed from “Company-staff” to “Platformindividual”, one may extend one’s ability to the extreme on the common social Internet platform and transform one’s working rule from fixing in definite organization to facing the whole society for one’s contribution, or from “passive” to “active”. One may rely on one’s social creditability, which is derived by social big data through Internet, and for obtaining one’s personal wealth and benefits following to the path of “behavior-competence-creditability-personality-wealth”. Along the path of “creativity-representation-displaying-ordering-productionclients”, the business rules are diversified. “Networking” will be no longer relationship between people under Internet era, but through definite “rules”. In Internet era social believing will be evolved along the path of “rule-order-morality-believing”, then the thinking philosophy and action of people will be greatly restrained by the Internet platform and internet environment. (4) Characteristics of Business Modes under Internet Era Industrial structure under Internet era is going to change from traditional one to Internet one, where the existing business mode have been heavily destroyed. The future tendency must be that, lower class enterprise involves in service, middle class in products and the highest enterprises are building internet platforms for the others. The social ecology will be the global platform involving each subordinate platform from companies, enterprises and organizations connecting to the government platform. Another characteristic of social business ecology is the “Co-existence of diversifying”. Despite of transverse development in the past, the enterprise will pursuing vertical development in their business pursuing preciseness and deepness. The enterprises are search their ways on individuality of their products through science and technology and on the connectivity to their specified clients through internet. Under Internet era, employment phenomena will be gradually weakening and partnership phenomena will be rising. The evolution of business and e-commerce are taking the route of B2B (business to business)—B2C (business to customer)— C2C (customer to customer)—C2B (customer to business)—C2F (customer to fabrication). The business is entering to the era of individualism and globalization. Furthermore there will be the evolution of Internet-movable internet and “internet of things” to form a comprehensive platform of Internet ecology for promoting the productivity of the society. Certainly, it will deeply influence to the managerial philosophy and managerial methods to the traditional project management. Consequently, the subversion of professional chain seems to be happened from the chain of “producer-agent-consumer” to the chain of “consumer-designer-producer”. Agent in the past business chain will be replaced by designer for satisfying numerous customized expectations and requirements from the consumer.

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(5) Subversion to Project Management Behavior Due to the emergence of Internet, the mode of traditional project management has changed, it will heavily influence to the theoretical frameworks of project management, program management and portfolio management. For project management, all the operation procedures will be completed through internet, the database (including cloud base) will be built and accessible to all procedures during the life cycle of the project, the real time organizational management of the project will be replaced by transferring of information between different departments hierarchically and makes it possible to accelerate the processes and thus rise efficiency of project management. For program management, it is possible to accessing millions of transactions (projects) in parallel with the strictness of reviewing, monitoring, controlling and updating rather than to handle just some projects in the past. The management philosophy and the methods of maneuvering have heavily changed. For portfolio management, it is also possible to compare almost infinite initiatives under strict criteria of selection, which is impossible to realize without a global Internet platform as the basis. All above mentioned statements show us that the emergence of Internet provides us the possibility to carry out comprehensive and highly efficient best practice of project management. Nevertheless, the modes and contents of project management under Internet era have been subverted; the corresponding theoretical framework of project management is subjected to rectification. (6) What shall PMO do? PMO is a leading strategic sector over all the departments and sections of an enterprise, which guides the strategy and principle of operation in every business activity of the enterprise. Certainly, under Internet environment, PMO needs to update and modify all the philosophical thinking in its operation for strategic guides accordingly. Internet era is inevitably approaching to our real life and causing dramatic change in project management. Based on successful best practice of numerous projects, we need to develop and conclude the essentials of PMO principles under the new tendency of Internet era and trying to develop a new standard or a new practice guide in “PMO under Internet Era” for accommodating the tendency of development of our era.

10.10 Summary We are entering in VUCA (volatility, uncertainty, complexity, ambiguity) era, the stiffened solution of project management problems are no longer appropriate rather than the softened solution. In other word, we need to involve soft skills in our project management, to add our strategy both in project management (PM) and project governance (PG), where the philosophy of “dialectic unification of two opposites” are necessary, for instance, eastern “Tiji Logic” (Ronggui, 2018) may help in this

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163

regard. Softening the management with soft skills will be the general tendency in the future. By the help of artificial intelligence technology, cloud calculation and big data exploration, it could replace human being to processing common management affaires, including decision making under changeable environment during different stages of the project management, and to determine the future development tendency according to actual situation, thus reveals the cordial changes in project management. Accommodating to the development of digital economy, each one in the society is a knowledge individual, essentially, is an independent “knowledge-economic entity”, they interact with each other in a defined job and play their specific role. Under such case, the social organization structure will lose their former style, and the real organization roles will no longer exist and replaced by the information re-production and transferring between the nodes in the hierarchical net of different functional sectors. Everyone, who involved in the project, is just playing their particular role to offer their contributions. Where, more authorization, fault tolerance and cooperation are needed. Decentralization is another character of project management in digital era, which intends open and transparency of managerial procedures, the application of “Block Chain” to project management is another perspective direction of its future development, this may strengthen the cooperative capacity of individual teams in the project to diversified distributive joint efforts. The “Block Chain” (Bo, 2019) may possibly become the core of next generation of Internet. Since it promotes further detailed division of duties and brings a technical revolution of Internet infrastructure, forms a new type of production relationship. Such like the revolution of container in last century, which promoted the changes of global economic pattern as well as the geopolitics, thus greatly promotes the economic globalization. If we recognize “Block Chain” as a new relation of production, which promotes further detailed technical division, reduces cost in collaboration, thus increases productivity of the society. Nowadays, we are facing a new technical revolution of “Block Chain”, and it is our believing that it may cause the effects beyond our imagination to our era such as in the project management. The roles and tasks of enterprise in knowledge economy is entire different from that of the enterprise in industrial economy, since the assets of the enterprise is no longer solid one as in the past, but the virtual one. The objectives of enterprise management should focus on how to realize the transformation of its virtual assets (knowledge of their staffs) to be a real one. Every effort from the enterprise is to stimulate the passion of its staffs for the willingness of contribute their knowledge into creative ideas, which become innovative features of their products and increase its market competitiveness; so as to increase the enterprise’s potentiality to win in the market competition. We are engaged in a great era, subject to the subversive challenges of digital era in project management, how to accommodating and push forward its healthy development? It is the mission of all of us for our great era!

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10.11 Conclusive Remarks ---Perspectives in Project Management under Digital and “Internet + AI” Era

It is going to the end of this book “Fuzzy Quantitative Management”, how shall we respond to the digital and “Internet + AI” era ? The impetus of this book is trying to make the preliminary answer to feed the students in their Project Management course with the ideas of involving the changes from Internet, for they can’t sustain themselves in coming Internet era but remain with the traditional theory of project management! Taking account of the changes by internet and digital era on project management nowadays, one needs to have such a transformation in mind. This monograph is based on system analysis and structured way for project implementation through strategic, tactic and operational approaches, which is based on the principles of “Softening of Theorem” and “Hardening of Experiences” in digital era. This book is different from conventional PM textbook, which strictly follows the Talor-based production line philosophy under industrial economy, and trying to illustrate those principles of digitization of management and facilitate the students with ideas to manage the project under Internet era. As for the commercial analyses of project, the book provides rather deepened analyses in project economics. In conventional contents of planning, scheduling, control and operation of project management, the book provides with advanced solving methodologies and illustrative samples. Except being a textbook for graduate students in related engineering management majors, for the purpose of improving maneuverability of complicated real projects, this book can also be used as a reference book for project management practitioners. Following to the environment changes in project management, it is necessary to introduce methods and tools for accommodating the new challenges to project management under digital Internet era – directly to its basic procedures, methods, tools and strategies. The characteristics of project management in its future development should be well studied; its perspectives need to be explored to the readers. A future picture is shown in this book that the operation of project management will be performed on a virtual platform to deal with virtual objects; that make big differences for every project management practitioner. We also provide concepts of future perspective that project management will be processed under the “Internet + AI” environment. i.e.; Based on comprehensive information collected from the Internet, AI technology is used for make a series of decision in each project stage, representing the implementation of project management. This concept is extremely important for those professional instructors of project management, who are going to cultivate future generation of PM talents. Applying innovative studies of project management under Internet, Internet of things, big data and artificial intelligence to promote the changes of project management contents, including traditional organization, resources, and other management elements, form the essentials of this book. How to transform the problem from unstructured opportunities and needs to structured project management framework

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under uncertain environment, has its broad prospects. In short, we should actively carry out our research works to form the system of project management theories, methods, and tools accommodating to the digital Internet era, thus promoting the development of the project management discipline. Available PM books are focused on traditional project management points of view under industrial economy, corresponding to the methods and tools operated on real platform and dealing with real objects; nevertheless, this book focuses on system analysis of project and structured implementation methodologies under digital knowledge economy, which is operated on the virtual platform and dealing with virtual objects. We try to explain project management nowadays in a way to soften the theories and harden the knowledge for accommodating the challenges from the new digital era.

References Ding Ronggui (2018) “Tiji Logic”: The Chinese intelligence in project governance. By China Electric Publisher. (in Chinese). Glatt, C. R., & Hague, D. S. (1975) ODIN: Optimal design integration system. In Prepared by Aerophysics Research Corporation, Hampton, Va.23666, for Langley Research Center. U.S.A. Hill G. M. (2007) The complete project management office handbook, 3rd edition, CRC Press. Jing Dong Group Co. (2017) “618”—The way we win! publishing house of electronics industry, Beijing, China, (in Chinese). Li Bo (2019) The block chain technology and its applications in engineering management, New Business Weekly, 2019, 8(1) (in Chinese. Lin, S., et al. (1987). Knowledge engineering and expert systems in structural engineering. Acta Aeronautica Et Astronautica Sinica, 8(7), A317–A326. (in Chinese). Shanghai Zhenhua Heavy Industry Co., Ltd. (ZPMC), (https://cn.zpmc.com/) Shopei Lin. (2008) Fuzzy-AI model for managerial science, keynote speech. In PMI World Research Conference, Warsaw, Poland. July, 2008. Shaopei Lin. (2018) The subversive changes facing to project management—have we ready enough? Journal of Project Management Review, 4(2), pp.12–15, 2018 (in Chinese). Sunder S. S. (1985) Knowledge-based expert systems for offshore engineering. In Proceedings. of 4th International Symposium on Offshore Mechanics and Arctic Engineering, Feb. 1985, Dallas, Texas, U.S.A. pp. 593–600. Wilhite, A. W., & Rehder, J. J. (1979) AVID—A design system for technology studies of advanced transportation concepts. [Aerospace Vehicle Interactive Design]. NASA Tech. Memo. NASATM--X-65485.