Logistics Operations and Management for Recycling and Reuse [1st ed.] 9783642338564, 9783642338571

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EcoProduction. Environmental Issues in Logistics and Manufacturing

Paulina Golinska-Dawson   Editor

Logistics Operations and Management for Recycling and Reuse

EcoProduction Environmental Issues in Logistics and Manufacturing

Series Editor Paulina Golinska-Dawson, Pozna´n, Poland

The EcoProduction Series is a forum for presenting emerging environmental issues in Logistics and Manufacturing. Its main objective is a multidisciplinary approach to link the scientific activities in various manufacturing and logistics fields with the sustainability research. It encompasses topical monographs and selected conference proceedings, authored or edited by leading experts as well as by promising young scientists. The Series aims to provide the impulse for new ideas by reporting on the state-of-the-art and motivating for the future development of sustainable manufacturing systems, environmentally conscious operations management and reverse or closed loop logistics. It aims to bring together academic, industry and government personnel from various countries to present and discuss the challenges for implementation of sustainable policy in the field of production and logistics.

More information about this series at http://www.springer.com/series/10152

Paulina Golinska-Dawson Editor

Logistics Operations and Management for Recycling and Reuse

Editor Paulina Golinska-Dawson Faculty of Engineering Management Pozna´n University of Technology Pozna´n, Poland

ISSN 2193-4614 ISSN 2193-4622 (electronic) EcoProduction ISBN 978-3-642-33856-4 ISBN 978-3-642-33857-1 (eBook) https://doi.org/10.1007/978-3-642-33857-1 © Springer-Verlag GmbH Germany, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer-Verlag GmbH, DE part of Springer Nature. The registered company address is: Heidelberger Platz 3, 14197 Berlin, Germany

Preface

The increasing awareness of the circular economy concept and the sustainability challenges are reshaping the logistics operations and the supply chain management principles. The emerging new resource-efficient business models allow reducing, reusing and recycling and therefore they support transition towards a “zero waste” economy. The focus on the resource efficiency and waste reduction is translated into searching new business opportunities through closing and narrowing the materials loops in the economy. The aim of this book is to present quantitative and qualitative aspects of logistics operations supporting 3R (reduce, recycle and reuse) policy. In individual chapters, the authors address various challenges related to reverse network configuration and the organization of collection, transportation and recovery activities. The authors in the individual chapters discuss the original methods and tools, as well as practical case studies on topics, as follows: • Circular Economy, • Reverse Logistics Flows and Network’s Configuration Problems, • Practical Aspects of Application the 3R (Reduce, Recycle, Reuse). In the first part the challenges of the circular economy with regard to the logistics sector and supply chain management have been discussed. The environmental impact of the logistics operations on the global scale (especially transportation) has been widely discussed in the scientific literature. However, the logistics providers link different companies in the supply chain therefore they have transformational potential towards circular economy at meso-scale. Most of the studies on circular business models focus on manufacturing and product design. In the chapter “Towards Circular Economy Transition—Developing the Innovative Sustainable Practices in Logistics Industry” the sustainable practices in the logistics sector are discussed and classified. The bottom-up approach is taken, in order to analyse how sustainable practices at micro-level have transitional potential towards circular economy. In chapter “A Literature Analysis of Definitions for a Circular Economy” are reviewed 28 different definition of the CE. The authors have highlighted the importance of stakeholder perspective in scientific discourse on the transition towards the circular economy. v

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In the second part of this book the focus is placed on the reverse logistics flows and network’s configuration problems. In the chapter “Robust Reverse Logistics Network Design” is presented a practically applicable optimization method for waste wood reverse logistics. The authors extend the local search heuristic for the facility location problem with nonlinear objective function that allows minimizing the costs. They discuss the results of numerical experiments with regard to the impact of the waste wood recycling on the CO2 emission. The authors of the subsequent chapter discuss “Drivers and Barriers for Cooperation Between Municipalities in Area of Municipal Solid Waste Management”. The empirical data from over 230 Polish municipalities is analyzed in order to conclude how the decisions to engage in such cooperation are facilitated. The authors of chapter “A Novel Formulation for the Sustainable Periodic Waste Collection Arc-Routing Problem: A Hybrid Multi-objective Optimization Algorithm” develop an effective methodology to determine the optimal plans for wastecollection routes and the required number of vehicles They propose mixed-integer linear programming model in order to obtain a multi-objective multi-trip sustainable plan for waste collection. In the subsequent chapter is presented “A Perishable Product Sustainable Supply Chain Network Design Problem with Lead Time and Customer Satisfaction using a Hybrid Whale-Genetic Algorithm”. The authors propose an integrated mathematical model that allows minimizing the production, distribution, and customer satisfaction related costs, minimizing total CO2 emissions, and maximizing social responsibility. The third part of the book includes selected practical aspects of application the 3R (reduce, recycle, reuse). The authors of chapter “How to Assess Internal Transport in Terms of Sustainability in the Recycling Industry?—Case Study” analyse the internal transport process in a company which specializes in End-of-Life Vehicles disassembling. They develop a heuristic to assess and improve the intralogistics operations. The chapter “Principle of the Cognitive Grinding of Reuse Materials” presents a multi-disc grinder of environmentally enhanced design. The authors propose for evaluation of structural system operational parameters, the criteria: energy consumption, material consumption, operational efficiency and effectiveness, the overall waste balance (waste balance, waste produced). In the final chapter “Smart-Tracking Systems Development with QR-Code and 4D-BIM for Progress Monitoring of a Steel-Plant Blast-Furnace Revamping Project in Korea”, the authors develop a smart tracking system with wireless tagging technology. They discuss the outcome of the application of the proposed solutions with regard to the reduction of costs and work load of workers. This book might be a valuable resource for both academics and practitioners who want to deepen their knowledge of logistics operations and management for recycling and reuse. This monograph is edited in association with the 15th International Congress on Logistics and SCM Systems (ICLS 2020). The ICLS 2020 is organized by the Faculty of Engineering Management, Pozna´n University of Technology and the International

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Federation of Logistics & SCM Systems (IFLS). I would like to express my gratitude to the Board of the International Federation of Logistics and SCM Systems (IFLS) for the invaluable inspiration and motivation to prepare this volume: • Honorary Chairman—Prof. Karasawa, Yutaka, Kanagawa University, Japan. • Advisors—Prof. Kachitvichyanukul, Voratas, Asian Institute of Technology, Thailand; Prof. Katayama, Hiroshi, Waseda University, Japan. • Chairman—Prof. Tsai, Kune-Muh, National Kaohsiung University of Science and Technology, Taiwan. • Vice Chairmen: Prof. Lai, Kin Keung, City University of Hong Kong; Prof. Liu, Xiaohong, Central University of Finance and Economics, China; Prof. Rim, SukChul, Ajou University, Korea; Sethanan, Kanchana, Khon Kaen Univesity, Thailand; Prof. Wakabayashi, Keizo, Nihon University, Japan and Wu, Yenchun Jim, National Taiwan Normal University, Taiwan. • The Board Members. This scientific monograph has been blind reviewed. I would like to thank all reviewers whose names are not listed in the volume due to the confidentiality of the process. Their voluntary service and comments helped the authors to improve the quality of the manuscripts. Although not all of the received chapters appear in this book, the efforts spent and the work done for this book are very much appreciated. Pozna´n, Poland

Paulina Golinska-Dawson

Contents

Circular Economy Towards Circular Economy Transition—Developing the Innovative Sustainable Practices in Logistics Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . Paulina Golinska-Dawson A Literature Analysis of Definitions for a Circular Economy . . . . . . . . . . . Usama Awan, Narmeen Kanwal, and Mohammed Khurrum S. Bhutta

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Reverse Logistics Flows and Network’s Configuration Problems Robust Reverse Logistics Network Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . Péter Egri, Balázs Dávid, Tamás Kis, and Miklós Krész

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Drivers and Barriers for Cooperation Between Municipalities in Area of Municipal Solid Waste Management . . . . . . . . . . . . . . . . . . . . . . . Paulina Golinska-Dawson, Arkadiusz Kawa, and Piotr Januszewski

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A Novel Formulation for the Sustainable Periodic Waste Collection Arc-Routing Problem: A Hybrid Multi-objective Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Erfan Babaee Tirkolaee, Alireza Goli, Gerhard-Wilhelm Weber, and Katarzyna Szwedzka

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A Perishable Product Sustainable Supply Chain Network Design Problem with Lead Time and Customer Satisfaction using a Hybrid Whale-Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alireza Goli, Erfan Babaee Tirkolaee, and Gerhard-Wilhelm Weber

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Practical Aspects of Application the 3R (Reduce, Recycle, Reuse) How to Assess Internal Transport in Terms of Sustainability in the Recycling Industry?—Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Izabela Kudelska and Monika Kosacka-Olejnik

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Contents

Principle of the Cognitive Grinding of Reuse Materials . . . . . . . . . . . . . . . 145 Adam Mrozi´nski, Józef Flizikowski, Kazimierz Bieli´nski, and Marek Macko Smart-Tracking Systems Development with QR-Code and 4D-BIM for Progress Monitoring of a Steel-Plant Blast-Furnace Revamping Project in Korea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 H. Y. Roh, E. B. Lee, I. H. Jung, and C. Y. Kim

Circular Economy

Towards Circular Economy Transition—Developing the Innovative Sustainable Practices in Logistics Industry Paulina Golinska-Dawson

Abstract The circular economy concept encourages the redesign of industrial activities and social practices in order to decouple the economic growth from negative environmental impact. The focus is placed on establishing more resource-efficient business models that allows reducing, reusing and recycling and therefore they support transition towards a “zero waste” economy. The environmental impact of logistics operations is formidable due to the GHG emissions, noise, and congestion and material waste. The logistics providers act, as the link between different companies in the supply chain therefore they have transformational potential towards circular economy at meso-scale. Most of the studies on circular business models focus on manufacturing and product design. The studies on logistics sector are very limited and this chapter addresses that research gap. The aim of this paper is to link the described in the literature circular economy business models with innovative sustainable practices in the logistics industry. The main contribution is the classification of the innovative technological, organizational and social sustainable practices in logistics sector. The bottom-up approach is taken, as it describes how sustainable practices at micro-level have transitional potential towards circular economy. Keywords Logistics service providers · Logistics infrastructure · Circular economy · Sustainability · Supply chain · Logistics

1 Introduction The Circular Economy (CE) objective is to highlight the interplay between the environment and the economic system (Ghisellini et al. 2016). It is rooted in a diverse theoretical background, such as: industrial ecology (Ayers et al. 1989), environmental economics (Pearce and Turner 1990) and industrial economics (Stahel 1982). It is influenced by different environmentally conscious theoretical concepts, such as: P. Golinska-Dawson (B) Faculty of Engineering Management, Pozna´n University of Technology, Jacka Rychlewskiego 2 str., 60965 Pozna´n, Poland e-mail: [email protected] © Springer-Verlag GmbH Germany, part of Springer Nature 2020 P. Golinska-Dawson (ed.), Logistics Operations and Management for Recycling and Reuse, EcoProduction, https://doi.org/10.1007/978-3-642-33857-1_1

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cradle-to-cradle (McDonough and Braungart 2002), performance economy (Stahel and Clift 2016), eco-efficiency (Huppes and Ishikawa 2005), life cycle management (Kirkke et al. 2004).

The CE approach contrasts with the linear economy model (take-make-usedispose) and provides new approaches to achieve benefits, as it buffers against the scarcity of fossil fuels and raw materials by recapturing and recovering resources and energy which are already embodied in the existing techno-social-economical systems. Circular Economy is “restorative and regenerative by design and aims to keep products, components, and materials at their highest utility and value at all times” (Ellen McArthur 2015, p. 2). The concept of Circular Economy (CE) in the last few years has been promoted by the European Commission (COM/2015/0614), as a new pathway for a more resource efficient and competitive economy. The circular economy concept encourages the redesign of industrial activities and social practices in order to decouple the economic growth from environmental burden and trigger a transition towards a “zero waste” economy. The European Commission has stated that “the EU has no choice but to go for the transition to a resource-efficient and ultimately regenerative circular economy’ (EC 2011 p. 1). The implementation of the CE at the local (called: micro-level, for example at a company) and regional level (called: meso-level, for example a supply chain) is challenging. At the same time the companies, (as the singular actor) own most resources and capabilities, therefore can stimulate CE transition by creating added value through an extended and more proactively managed stakeholders’ network (Geissdoerfer et al. 2016). The logistics providers act, as the link between different companies in the supply chain therefore they have transformational potential towards circular economy at meso-scale. However studies on the logistics sector with regard to the logistics sector are very limited. The circular economy can be analyzed, as a part of ecological modernization theory (EMT). The EMT reconciles the conflict between industrial development and environmental protection (Murphy and Gouldson 2000) by “ecologizing economy” and “economizing ecology”. The EMT stipulates that sustainability transition can be achieved by increasing resource productivity, improving environmental sustainability while retaining production and consumption. The EMT “can be used to help corporate managers understand and guide ecologically oriented management innovation and change, at both the firm and supply chain level of analysis” (Park et al. 2010. p. 1495). The technological and organizational innovations can result in the redesign of products and services for reuse and easier value recovery in multiple life-cycles, resulting in new relations between stakeholders in the supply chain. The transition towards more sustainable practices requires to identify and address opportunities and concerns, and to encourage “leadership thinking and best practice and to provide a forum for policy innovation” (Hobson 2016, p. 94). The innovative practices can allow for the reduction in resource consumption and lead to resource-conservative (Rashid et al. 2013), or resource-efficient business models. The critics of that approach postulate that it is necessary to move ‘beyond

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incremental efficiency gains to deliver transformative change’ (Preston 2012, p. 1). They suggest sufficiency or de-growth, as main drivers for change. In the last five years, volume of CE related literature has been rapidly growing but the studies on the logistics and supply chain management are still underrepresented (Govindan and Hasanagic 2018). The supply chain perspective with regard to the circular economy is addressed by relatively few authors (e.g. Geissdoerfer et al. 2018; Govindan and Hasanagic 2018; Ghisellini et al. 2016; Genovese et al. 2017; De Angelis et al. 2018; Hussain and Malik 2020). De Angelis et al. (2018) have stated that, there are very limited studies which investigate the relevance of supply chain innovations and their transitional potential towards a more resource efficient and circular economy. The role of the logistics sector in transition towards CE is even less addressed in the literature. Van Buren et al. (2016) have presented a pioneering work in that field and have discussed the role of the Dutch logistics sector in transition towards CE. The aim of this paper is to link the described in the literature circular economy business models with innovative sustainable practices in the logistics industry. Its main contribution is the classification of the innovative technological, organizational and social sustainable practices in logistics sector. The bottom-up approach is taken, as it describes how sustainable practices at micro-level have transitional potential towards circular economy. The research questions are as follows: RQ 1. What sustainable practices are implemented by the logistics providers (from 1PL to 5PL)? RQ 2. How the sustainable logistics practices can be linked with regard to the circular economy business models? The remainder of the paper is organized, as follows, first are discussed the related studies on the circular economy, innovative practices and circular business models. Then the role of the logistics services providers is discussed in transition towards CE. Finally the innovative sustainable practices in the logistics industry are classified and linked with the described in the literature circular economy business models.

2 Related Studies The differences and similarities between sustainable development and the circular economy approach are still not clearly stated in the body of literature. Both concepts focus on the importance of integration of environmental and social aspects with economic progress. They emphasize the transition at the system-level and call for innovations. The CE can be treated, as a precondition for sustainable development (Rashid et al. 2013), or the main solution for transformation (Jawahir and Bradley 2016). Geissdoerfer et al. (2017) have conducted an extensive literature review to investigate the relationships between sustainability and the CE. They classify those relations in three categories, as follows:

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• conditional, • beneficial or • trade-off. Beneficial relationship means that the CE is an option to foster sustainable development and establish more sustainable business models by increasing resource efficiency and dematerialization (Bocken et al. 2014; Weissbrod and Bocken 2017). Murray et al. (2017) provide some critical assessments of the relation between the CE and sustainability and identified the trade-offs. They have stated that the CE has a positive impact on some aspects of sustainability but at the same time it underestimates the importance of the social dimension of sustainability. Some authors also highlight that focus on the 3R or 6R (reduce, reuse, recycle, remanufacture, repair, refurbish) can lead to additional costs and environmental burden by low technical feasibility of closing the materials loop, increased transport operations in reverse logistics, or energy inefficiencies in the recycling process (Allwood 2014). The most common definition of sustainable development (from Brundtland 1987) provides an interpretative flexibility of its paradigms. The critics often state that the sustainable development concept is not precisely defined and it lacks the implementation framework, which influences its operationalization, especially at the company level (Geissdoerfer et al. 2017; Golinska and Kuebler 2014). The CE concept refers mostly to the individual economic benefits “through input reduction, efficiency gains, and waste avoidance with relatively immediate results compared to sustainability” (Geissdoerfer et al. 2017, p. 766). The economic benefits (economizing of ecology) attract the participation of the stakeholders in the CE transition. The CE derives from the industrial ecology (IE) paradigm that emphasizes the benefits of recycling of waste materials and by-products in order to minimize resource utilization (Andersen 2007). The IE’s “industrial metabolism” promotes the close cycle of materials and energy flows (Ayres et al. 1989). The studies on the application of the CE concept in practice have highlighted the material flow perspective, as: • implementation of closed loop materials flow in the whole economic system (Geng and Doberstein 2008); • implementation of the circular (closed) flow of materials and the use of raw materials and energy through multiple phases (Yuan et al. 2006); • application of the 3R principles: reduce, reuse, and recycle (Yuan et al. 2006; Sakai et al. 2011; Preston 2012) in production, consumption and services. Ghisellini et al. (2016, p. 11) have state that the adoption of the CE concept at a company level effects increased stakeholders (producers and consumers) responsibility and awareness, with regard to “the use of renewable technologies and materials (wherever possible), as well as the adoption of suitable, clear and stable policies and tools”. Moreover stakeholders’ involvement allows for creating additional value through collaboration and contributes new exchange patterns in the supple chain (Ghisellini et al. 2016).

Towards Circular Economy Transition—Developing … Fig. 1 The elements of the CE transition (micro and meso level)

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Sustainable innovative practices

Business models for closing materials loops & resource efficency

In Fig. 1 is presented the summary of the elements of the bottom-up CE transition from a company perspective. The aspects related to the CE governmental policy (top-down transition) are not covered in the scope of this paper.

2.1 Innovative Practices The circular economy pushes the frontiers of environmental sustainability at a company and in a supply chain. This is achieved by a redesign in materials flows by innovations. The transition beyond the delayed cradle-to-grave material flow can be established by transforming business models into innovative self-sustaining supply chains (Genovese et al. 2017). The challenges of the transition towards CE are heterogeneous and in order to meet them a multi-perspective approach is required. The multi-level model of innovation (Rip and Kemp 1998) covers a macro level of the sociotechnical landscape, the meso level regime and the micro level niche. It advocates that transition processes of co-evolution happen within and between these layers. The transition might happen by different agents who are “shaping or making niches and paths” (Berkhout et al. 2004, p. 50). According to Kemp et al. (2007, p. 1) transition management is “a multilevel model of governance which shapes processes of co-evolution using visions, transition experiments and cycles of learning and adaptation”. The existing policy frameworks are often fragmented and for that reason are suitable to deal with complex sustainable development problems. Transition management creates space for innovative governance at local, regional and national level. It combines top-down and bottom-up approaches to fostering the transition. The transition is often triggered by forerunners, who are acting autonomously from the current dominant regime (meso-level). The empowerment of the first movers is crucial and can be achieved by a joint policy agenda; financial incentives, innovations, and small-scale experiments (stakeholders’ practices). Transition trajectories can also be triggers by governance practices at the local, regional and national level. The

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transition towards the CE is “predominantly issues of innovation, technical systems, and fiscal and reformulated business models” (Hobson 2016, p. 89). The transitions towards Circular Economy in logistics industry can be perceived as an outcome of “multi-dimensional interactions between radical niche-innovations, an incumbent regime, and an external landscape” (Verbong and Geels 2010, p. 1215). The multi-level perspective on social-technological transition assumes that innovation is a pre-condition of systemic change. The innovation starts at isolated innovative initiatives at local level (so called niches), spreading to society as a whole and, finally, acquiring transformative scale (so called regime level). The innovative sustainable practices are created by the pioneers in the niches (micro-level). The innovation results from market niches or technological niches. The innovative practices usually are forested by the “protection from small networks of actors who are willing to invest in the development of new technologies” (Verbong and Geels 2010, p. 1215). The scaling up of the local sustainable practices results in transition at meso-level and finally macro-level (the socio-technical landscape).Those levels with regard to the logistics industry can be defined: 1. Micro-level: single logistics services providers (niches) 2. Meso-level: spreading of initiatives resulting in wider changes in the sector (patchwork of regimes) 3. Macro-level: paradigm-shift penetrating the entire “landscape” of society contributing towards circular economy (Fig. 2). There are different pathways for transition based on the reinforcing or disruptive relations between “niche innovations” and the existing “socio-technological regime”, they can be classified as follows (Geels and Schot 2007): • transformation, when the existing regimes are subject to external pressure (by different stakeholder from the external landscape), therefore there is need for the adjustment and reorientation of existing regimes. It is caused mainly by pressure on the new regulations and new systematic solutions. The innovations are limited to the niches;

Fig. 2 The multilevel—perspective, developed based on Verbong and Geels (2010, p. 1215)

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• reconfiguration, when selected niche-innovations are developed (as regimes face problems and external pressures) and incorporated, as add-ons or component’s substitutions, in order to allow the gradual reconfiguration towards “new regime”; • technological substitution, when new technologies gain more momentum (so called “niche-accumulation’), and penetrate bigger markets, eventually replacing the existing “regime”. • de-alignment and re-alignment, when the uncertainty rises due to the destabilization of the existing regimes. The multiple niche-innovations are observed and after some period of time one option becomes dominant. The circular economy transitions requires simultaneous shift in the consumers’ behavior, governmental policies and business practices (Van Buren et al. 2016). In this paper the focus is places on the business model and innovation perspective. Bidmon and Knab (2018) have stated that the business model can contribute in transitions, as: • business models facilitate the process of technological innovation and allow to expend them from niche to “regime level”, • novel business models drive transitions without relying on technological innovation.

2.2 Circular Business Models Studies on circular business model gain a lot of popularity among academia in the last decade. A business model describes how a company creates, delivers, and captures value (Osterwalder 2004). The business model serves the company to commercialize the product and technology innovations (Chesbrough 2010), as they facilitate better planning, and communicating in face of the increasing complexity of organizational configurations and activities (Osterwalder and Pigneur 2010). It is a “like a blueprint for a strategy to be implemented through organizational structures, processes, and systems” (Osterwalder and Pigneur 2010, p. 10). Boons and Lüdeke-Freund (2013) have stated that business models allow bringing the sustainable innovation to the market by combining a value proposition, with the upstream and downstream value chain and a financial model. They have distinguished the elements of the business model, as follows (Boons and Lüdeke-Freund 2013): • value proposition, that defines value, which is embedded in the offered product/service; • supply chain, that describes how are structured and managed the upstream relationships with suppliers; • customer interface, that describes how are structured and managed the relationships with customers; • financial model: that covers costs and benefits and their distribution between stakeholders.

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Summarizing, the business model is implemented: • to provide the value for stakeholders by delivering products and or services; • to create cooperation downstream and upstream in the supply chain; • to capture value and sustain the financial stability. Circular business model (CBM) describes business model that supports transition towards the Circular Economy at a company level taking into consideration the resources and capacities. Bocken et al. (2016) have stated that such a business model allows to: slow, narrow, and close resource loops, therefore it is possible to reduce resource input into the company and to minimize waste and emissions leakage. Geissdoerfer et al. (2018) have extended the above mentioned classification by adding two more circular business models. They have highlighted to importance of intensifying the usage phase of the existing resources, and the substitution (where possible) of product utilization by service and software solutions (so called dematerializing). The CBM aims to create the monetary and non-monetary value by innovations (technological, organizational and social) and pro-active management of a multiple stakeholders. The next section of the paper present the relation between the circular business model and transition research with regard to the logistics industry.

3 Transition Framework The sustainable practices are described usually at a single company or in the supply chain context. The sustainable practices in the supply chain can be divided into: • • • • •

sustainable procurement (sustainable purchasing); sustainable warehousing; sustainable transport; sustainable packing; reverse logistics.

The implementation of the circular economy principles in the supply chain can be translated into different practices, for example such as: • • • • •

reverse logistics, creating suppliers’ communities (industrial symbiosis), local sourcing or dual sourcing with secondary materials, product service systems (PSS), sharing of infrastructure.

The existing body of literature provides very limited discussion on the perspective of the logistics service providers (LSPs). The logistics service providers’ (LSPs) link different stakeholders (manufacturers, retailers, consumers etc.) in the supply chain, therefore they have potential to facilitate the diffusion of the sustainable practices and

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transition towards the CE. In the logics industry the logistics services providers (3 PLs -5 PLs) coordinate and orchestrate the downstream and upstream relationships in the supply chain, therefore enabling the diffusion of the innovative practices at the intra-organizational level. PLs face a lot of challenges related to: globalization, pressure on the increased business-to-business relations in production–consumption networks, development of new disruptive technologies that may reconfigure the existing business networks (e.g., 3D printing, IoT), increasing importance of after sales services and reverse logistics. Moreover the expanding e-commerce has resulted in a complex last mile distribution and related reverse flow of commercial products, so called first mile distribution (Van Buren et al. 2016). The first research question in this paper is stated, as: RQ1: What sustainable practices are implemented by the logistics providers (from 1PL to 5PL)? The LPSs can be characterized with regard to sustainability, as follows (based on Gruchmann 2019): • Single Service Provider (1PL)—a company offers a single logistics service (transport, warehousing etc.) usually as subcontractor. It implements the sustainable practices in order to meet the CO2 emissions target by reduction of its environmental impact of logistical assets (e.g. using HGVs with cleaner drive, increased energy efficiency, etc.). • 2nd Party Logistics Provider (2PL)—a company offers all classical logistics functions (transportation, handling and warehousing) and usually it uses more than single transport mode. It implements the sustainable practices by using multimodal transport, and the selection of the best modal split allows reducing the environmental impact of its logistical activities. • 3rd Party Logistics Provider (3PL)—a company offers extended services in comparison to the 2PL by creating the added value to customers by cross docking, inventory management, co-packaging etc. Usually it is a globally acting company. It has got sufficient financial resources to invest in the advanced decision support systems dedicated to lower environmental impact of its operations (GDSS) by optimizing transport mode, route and capacity usage. Usually 3PL also implements and communicates to the customers the social corporate responsibility standards (CSR). • 4th Party Logistics Provider (4PL)—a company acts, as an integrator in the supply chain and manages the network of 3PLs. It acts, as one-stop-shop and often it operates without owning logics infrastructure (so called non-asset-owning service providers). The sustainable practices focuses on the achieving greener supply chain configurations, and ensuring meeting the CSR standards among the participants. It also can integrate all reverse logistics flow in the supply chain (first mile logistics). • 5th Party Logistics Provider (5PL)/Lead Logistics Provider—a company offers services that focus on the design and management of the supply networks. It

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focuses on the e-business support. It implements the sustainable practices by integration of the commodity flows and reverse flows by implementing the sharing and circular economy principles. Gruchmann et al. 2018 has also proposed to add to the above classification the Lead Sustainability Service Provider (6PL). That terminology is not yet well established in the literature or practice and for that reason it is not included in this paper in the further considerations. The previous studies (e.g. Gruchmann 2019; Gruchmann et al. 2018; Govindan and Hasanagic 2018; Genovese, et al. 2017; Van Buren et al. 2016) have identified the number of the practices, drivers and barriers for the development of the sustainable practices in the supply chain. Table 1 presents the summary of the sustainable practices from the literature review. The practices are clustered into technological, organizational and social category. As, the PLs link other in the supply chain it is assumed that the scaling up of sustainable practices in logistics sector is related to the enablers that are classified four dimensionally, as: • • • •

HI—horizontal integration (with other companies in logistics sector); VI—vertical integration (upstream the supply chain); CI—customers integration (downstream the supply chain); RI—reverse flow integration (RL). The above mentioned practices can be summarized, as:

• avoidance of the unnecessary transport, increasing utilization of existing resources and usage of cleaner (low emission) technologies (practices: T.1–T.6, T.10–T.12, O.1–O.6, O.8, S.4); • city logics (practices: O.7; T.9, S.1–S.3); after sales services and reverse logistics (practices: T.7, T.8); • after sales services and reverse logistics (practices: T.7, T.8); • recovering and re-using resources (practices: O.9, O.10). The avoidance of unnecessary transport can be achieved by optimization of product flows towards the consumer/end user, using advanced IT systems in order to optimize routes and drop offs, as well as environmental friendly and energy efficient transport modes (and combination of them). The infrastructure sharing and innovative ownership models (e.g. pay for use, PPS etc.) allow for increasing the load factor of fleet and higher utilization rate of the other infrastructure (e.g. distribution centers). The LPs can act also as facilitator towards industrial symbiosis (relocation of the companies into industrial/eco parks) and local sourcing. The material flow perspective (through the logistics hubs) allows relating the local infrastructure with the meso-level perspective (the supply chain). The innovation in the material flows can lead to the spatial reconfiguration of the stakeholders in supply chain (e.g. moving from current location to the industrial park). The industrial park can contribute to the sustainable development in the future as “as companies can co-locate to facilitate the material interchange and ‘energy cascading’ necessary for closed productionconsumption loops (Gibbs et al. 2005, p. 174). According to the critics of the CE

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Table 1 The sustainable practices in logistics industry, developed based on Gruchmann (2019), Gruchmann et al. (2018), Genovese et al. (2017), Van Buren et al. (2016) Sustainable practice

Enabler 1PL 2PL 3PL 4PL 5PL

Technological T.1 Multimodal transportation

HI

–

+

+

+

+

T.2 E-mobility

HI

–

–

–

–

+

T.3 Implementation of cleaner technologies (e.g. hybrid, VI bio fuels, hydrogen, LNG, Power-to gas etc.)

+

+

+

–

–

T.4 Advanced digital solutions (e.g. Green Decision Support Systems GDSS)

VI/HI

–

–

+

+

+

T.5 Last-mile bundling of cross company commodity flow

VI

–

–

–

+

+

T.6 Last-mile DSS (for decreased travel distance and increased the drop-off rate)

VI

–

+

+

+

+

T.7 First-mile integration of reverse logistics

RI

–

–

–

+

+

T.8 Sustainable packing

VI/RI

–

–

+

+

+

T.9 Alternative transport modes in urban logistics (e.g. cargo bikes)

VI

+

+

+

+

+

T.10 Join development of new technologies (e.g. co-financing)

VI/HI

–

–

–

+

+

T.11 Subcontracting platforms

HI

+

+

+

+

+

T.12 Increased energy efficiency

VI/HI

+

+

+

+

+

O.1 Shared used infrastructure (e.g. warehouses)

HI

+

+

+

+

+

O.2 Reconfiguration of the stakeholders (e.g. relocation to industrial/eco-parks)

VI

–

–

–

+

+

O.3 Product Service Systems PSS (leasing, renting, pay per unit, etc.)

HI

+

+

+

–

–

O.4 Cross-company use of vehicles

HI

+

+

+

–

–

O.5 Eco-driving training for drivers

HI

+

+

+

–

–

O.6 Consolidation commodity flow

HI

–

–

–

+

+

O.7 Micro hubs and depots

HI/VI

–

–

+

+

+

O.8 Implementation of environmental standards

HI/VI

–

+

+

+

+

O.9 Dual sourcing (e.g. purchase of remanufactured, recycled products for servicing purpose)

VI

+

+

+

–

–

+

Organizational

O.10 Support for local sourcing Social S.1 Crowd logistics solutions

CI

–

+

+

+

S.2 Consumers empowerment

CI

–

–

+

+

+

S.3 Building customers’ networks/communities

CI

–

–

+

+

+

S.4. Implementation of CSR

HI/VI

–

+

+

+

+

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P. Golinska-Dawson

approach e.g. (Allwood 2014), the CE practices in material flow management in a company or group of companies (a supply chain) might lead to an increased negative environmental impact. Therefore the impact of the local and regional transport on the environment can be increased, so use of eco-friendly and energy efficient technologies should be ensured. The sustainable practices in city logics are focused mainly on the last mile alternative transport modes (e.g. cargo bikes), innovative (however still unregulated and therefore controversial) crowd logistics solutions. The underdeveloped potential is laid in the emerging integration of the first and last mile logistics with regard to the e-commerce in urban/metropolitan areas. The new sustainable practices are emerging that allow for recovery and re-using the resources (e.g. usage of the remanufacturing components in the fleet servicing). Moreover LSPs can play a vital role in providing services in order to become the a “focal point” for return flows, facilitating economy of scale in recycling, re-use, the supply of new biodegradable raw materials or raw materials for 3D printing (Van Buren 2016). The shift towards circular business models and related innovations (technological, social, organizational) may allow to change the existing practices in the utilization of logistics infrastructure (like for example: networking, infrastructure sharing, shift from ownership to availability models, dual sourcing with primary and recycle materials) and management of materials flow (closing, narrowing or slowing the materials loop). Van Buren et al. 2016 has stated that logistics sector should change the focus from the services dedicated to moving goods (from A to B) to innovative services that becoming an indispensable enabler of circular economy through adding value service to production–distribution–consumption chains of other industries. The current body of literature doesn’t sufficiently link the sustainable practices and circular business models with regard to the logistics industry. Most of the CBM which are described in the literature focus on the products’ perspective not services’ perspective. Therefore the second research question aims to fill that gap. It is formulated as: RQ 2. How the sustainable logistics practices can be linked with regard to the circular economy business models? The CBM models are considered, that are described in the previous section. In Table 2 are linked the sustainable practices in logistics industry with the CBMs. The codding of the sustainable practices is the same, as in Table 1. The majority of the proposed practices support CBMs for the resource efficiency and intensifying usage of the existing resources. Currently the logistics infrastructure is used at relatively low rate. The material flow perspective (flow of products through the hubs) allows relating the local infrastructure with the meso-level perspective (the supply chain). The technological, organizational and social innovations can result in the redesign of services for reuse and easier value recovery in multiple life-cycles, resulting in new relations between stakeholders in the supply chain. The

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Table 2 Sustainable logistics practices with regard to the CBM, developed based on Bocken et al. (2016), Geissdoerfer et al. (2018) Circular business model

The goal for logistics industry

Sustainable practice

Slowing the resource loop

The focus is placed on the O.3; O.9 business practices that allows for extended useful life period (e.g. through repair, remanufacturing) of logistics infrastructure and fleet

Closing resource loops

The focus is placed on the business practices that allow for recycling and reuse of the recycled materials in next life cycles

Resource efficiency or narrowing resource flows

The focus is placed on the T.2; T.3; T.6; T.10; T.12; O.2; business practices that allow to O.3; O.5; O.8 O.10; S.4 reduce the utilization of resource needed with regard to the providing services for the customers

Intensifying the usage phase of the resources

The focus is placed on the business practices that allow to eliminate the inefficiencies in utilization the use of resource (e.g. by sharing, subcontracting) needed with regard to the providing services for the customers

T.1; T.5; T.9; T.11; O.1; O.4; O.6; O.7

Dematerializing

The focus is placed on the business practices that allow for decoupling the resources from service provided for the customers

T.4; T.6; S.1; S.2; S.3

T.7; T.8

transition towards CE (at meso-level) might leads to increased role of the local sourcing therefore reducing long-haul transportation. Taking into consideration the “multi-perspective transition theory (as described in the Sect. 2) two pathways towards CE are supported by the existing sustainable practices in the logistics sector, namely: • reconfiguration, when selected innovative sustainable practices (nicheinnovations) will be incorporated, as add-ons or component’s substitutions, in order to allow the gradual reconfiguration towards circular business models in the industry, especially with regard to sharing the logistics infrastructure, moving from infrastructure ownership towards pay-for-use (PSS), therefore leading to resource efficiency and eco-friendly intensification in usage of the existing vehicles fleet (increased load factor) by implementation of the advanced GDSS and

16

P. Golinska-Dawson

bundling cross company product flow and/or bundling the reverse logics flow from consumers. • technological substitution, when new technologies (e.g. e-mobility, 3-D printing) gain more momentum (so called “niche-accumulation’), and allow penetrate bigger markets, eventually replacing the existing “regime” in logistics industry.

4 Conclusions and Further Research In this paper the focus is placed on the discussion on the role of the logistics service providers with regard to the CE. This topic is underrepresented in the literature. The logistics services providers play dual role with the regard to the CE. First they may implement the CBM on the organizational level (micro-level), secondly they may facilitate (act as focal point, or coordinator) for the supply chain transition towards CE (meso-level). The main contribution of this paper the classification of the innovative technological, organizational and social sustainable practices in logistics sector and then linking them to the circular business models. The multi-perspective transition theory has been used in order to allow for discussion on the potential transition of the logics sector towards CE. The bottom-up approach has been used in order to highlight how local innovations in the niches (at micro-level) can contribute to scaling up transition in the sector. Limitation of this research is the focus on the bottom-up approach. The impact of the governmental policy and tools on the development of the sustainable logistic practices is not investigated in this paper. The considered enablers are limited to the integration with other stakeholders (other companies in logistics sector, customers, suppliers and manufacturers). The further research will focus on the empirical studies among the logistics service providers in order to verify to importance of link between the sustainable business practices and CBMs. Moreover the enablers for the logistics sector towards circular economy will be further investigated.

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A Literature Analysis of Definitions for a Circular Economy Usama Awan, Narmeen Kanwal, and Mohammed Khurrum S. Bhutta

Abstract This chapter aims to identify and analyze the published definitions of circular economy (CE). Twenty-eight definitions were gathered through intensive critical literature review, using both Scopus and Web of Science. The definitions developed from peer-reviewed literature analysis covered a period from 1999 through 2019, although most definitions were published from 2011 onwards. CE received significant attention in the early 90s and now is considered a mainstream strategy for product design and social, economic and environmental sustainability. Given that research is still relatively new in the sustainable circular economy. CE offers a reverse resource regenerative idea to eliminate the linearity of production and consumption system to support sustainability objectives. The CE definition analysis revealed that so far, resilience and stakeholder perspective is not explicitly included in the definition, although stakeholder is considered to be part of a natural and ecology system. CE has an impact on different aspects of the business throughout the entire supply chain. The concept of a CE is a value-orientated resource transformational process. CE considers both upstream and downstream production and consumption patterns to promotes the resilience orientation of resources. Currently, CE practices are carried out—meso, micro and macro. This chapter highlights that suggest that it is imperative to consider exosystem and chronosystem to better move away from linear to circular economy. Keywords Circular economy · Resource regenerative · Product design · Stakeholder’s perspective · Supply chain U. Awan (B) Industrial Engineering and Management, The Lappeenranta-Lahti University of Technology LUT, Lappeenranta 5385, Finland e-mail: [email protected] N. Kanwal University of Trier, Weidengraben 90, A18, 54296 Trier, Germany e-mail: [email protected] M. K. S. Bhutta College of Business, Ohio University, 522 Copeland Hall, Athens, OH 45701, USA e-mail: [email protected] © Springer-Verlag GmbH Germany, part of Springer Nature 2020 P. Golinska-Dawson (ed.), Logistics Operations and Management for Recycling and Reuse, EcoProduction, https://doi.org/10.1007/978-3-642-33857-1_2

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1 Introduction Global warming and climate change issues are gaining popularity, thus the managers of supply chains are faced with the challenge of how to integrate the circular economy (CE) practices into the production and management of their organisations. The CE practices are a way forward to lowering global warming, greenhouse gases, and minimizing air pollution by integrating sustainability objectives into the design of operations and products. There is a growing realization that companies must direct the issue of resource scarcity and dematerialization in their product physical life cycle. Since the inception of the sustainability concept in the 1980s, debates on the application circular economy has grown significantly (Awan et al. 2020). CE is commonly defined as resource reduction to meet the sustainability agenda of the existing and future generation. Recently, CE has been the focus of attention from practitioners and academicians. There is increasing recognition of CE on the political agenda of nations and particularly in Europe (European Commission 2015). CE is expected to play a vital role in the move towards economic development by generating new business opportunities, saving material cost, improving the security of future supply, while at the same time move towards a sustainable future (Kalmykova et al. 2017). CE is commonly considered as a solution for environmental protection and economic growth by utilizing resources in circularity. CE is understood as the “realization of closed-loop material flow in the whole economic system” (Geng and Doberstein 2008). In CE definitions confusion arises frequently surrounding the principles of 3R (reuse, reduction and repair). The term CE has been interpreted in a variety of different ways, ranging from closed flow of material to a “spiral-loop system” (Stahel and Reday-Mulvey 1981; Yuan et al. 2006). Previous CE definition tends to focus on transformation function of resources in the value chain (EllenMacArthur Foundation 2013), but as research expands, they are adopting CE concepts to sustainable development (SD) (Kirchherr et al. 2017b). There has been an attempt to establish a preferable grasp understanding of the CE and the application of SD in an industrial ecology context. CE has been defined in various ways, with one possibility “optimising consumption of resources and patterns, and redesigns the industrial system at the system level” (European Commission 2014). In a broader perspective, CE signifies the resilience, resource efficiency, and a restorative economic system. Perhaps the best known was by Stahel and Reday-Mulvey (1981), who defined CE in academic literature, as a “closed-loop economy”. The management of the CE is receiving increasing recognition. A number of recent literature reviews on the CE definition have been published including Ghisellini et al. 2016; Lewandowski 2016; Lieder and Rashid 2016; Sauvé et al. 2016; Blomsma and Brennan 2017; Geissdoerfer et al. 2017; Kirchherr et al. 2017b; and Murray et al. 2017. These literature reviews are indeed necessary in order to further develop the concept and transparency regarding the existing understanding of CE (Blomsma and Brennan 2017; Kirchherr et al. 2017a). However, they merely present an account of available definitions, basis to understand how CE is currently constructed at the macro, meso and micro level.

A Literature Analysis of Definitions for a Circular Economy

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There is an ongoing discussion about the significance and application of CE in a business context. However, the CE definition has not been explored in-depth, there are numerous and varied definitions of CE (Lieder and Rashid 2016). There is no broadly agreed definition of CE in the literature (Yuan et al. 2006). While Kirchherr et al. (2017b) write that no one single definition of CE has gained international acceptance. Merli et al. (2018) have pointed out the need for developing broader and more dynamic definitions and associated practices, in which they viewed CE as a system in which the value of the materials, products and resources is maintained in the entire product lifecycle. A specific conceptual and theoretical development might be required for a better understanding of the concept in discourse. As far as, there is no comprehensive definition of the CE, however, CE is often studied with an explicit definition of the concept (Blomsma and Brennan 2017). A recent study on the conceptualization of CE through analyzing definitions by Kirchherr et al. (2017b) focusing on broadening the concept of the CE among scholars and practitioners. CE is defined in so many different perspectives that it is difficult to come up with a consensus single definition. One possible explanation as to why there is no consensus regarding the definition of the CE could be due to the differences of conceptualization of various members about the implementation of CE in the context of reducing, reuse and recycling. These challenges potentially identify a need for a new definition of CE, as reported by the previous discussion in the literature. Firms employing CE approaches would also want to seize the potential benefits and opportunities offered by making use of waste prevention strategies before the design of the product. It is therefore important to discover what has not been addressed in the previous CE definitions and identify the knowledge gaps to suggest a new definition in a system perspective. This gap in the literature provided the impetus for the analysis of CE definitions by specific characteristics and measures, which is imperative for achieving sustainable growth. This study is distinct from previous research studies in two aspects. First, the study used keywords search from Scopus and Web of Science from the period of 1999– 2019. Second, the study addressed key CE characteristics assessed by the definitions in the literature. This study provides an overview of CE approaches development and analyzes the previous literature on circularity in firms to provide a shared definition and provides a series of future research avenues. Thus, the purpose of this paper is to identify and analyze how CE is defined in the existing literature and suggest future research direction. We have chosen the systematic literature review approach and summarize the salient findings of previous studies. The contribution of this study is two-fold: first to provide an update to literature review on CE, as well as to document the strengths and weakness of definitions in said literature. Second, to structure the literature in a way to provide a basis for the development of a comprehensive definition for a CE and point for future research in these areas.

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1.1 Systematic Literature Review Methodology The systematic literature review was limited to articles that were published in English peer-reviewed journals during the last 20 years (1999–2019). We consider this timespan to the possible inclusion of the articles published during this time. We include Scopus and Web of science our main identification source of articles. We selected both databases due to its broad coverage in engineering and technology journals. Since Scopus does not cover every peer-reviewed publication, it is possible to relevant papers published in the domain of circular economy might not miss. So, to avoid this, we also consider the web of science to search the related published work. To identify the relevant articles, we followed a structured pre-defined keywords search. Accordingly, the terms, “circular economy” and “Cradle to cradle” and “Closed-loop” were separately searched. The search word conducted along with the key terms “definition”, “defined”, define” and “refers” by utilizing “all field category” search in title, abstract and keywords. The data range to search the key terms were set for the papers published from 1999 to 2019 by utilizing the subject areas category. As of October 7, 2019. The systematic literature review approach using keywords has been used previously in many studies. In this present study, to identify the most relevant articles, we decided to reduce the number of articles for the review following three additional criteria for our literature review on circular economy and cradle to cradle approach. First, to be included in our review, the title of the article must deal with the circular economy concept. Second, an abstract of the article must reflect the circular economy as the main concept related to the reuse, repair and recycling. Finally, the journal in which article appeared must have been ranked in the source database. After applying such criteria and removing duplicates articles that were appeared in a web of science and Scopus data sources. A total of 1134 articles identified in the engineering, environmental and management science. As of 347 duplicates articles were excluded from the review at the initial stage. we then eliminated 411 articles that did not fit these criteria, it led to an initial number of 376 articles. As a result of this approach, our overall sample included 177 articles. After reading this article in-depth, we became familiar with this concept. We also noted that circular economy approaches have also appeared in published reports which have not been appeared in our keyword research. We carefully read these published working papers and decided to include in the references section. Given the scope and space consideration for this published working papers, however, we present a brief view of definitions that have been appeared in this literature.

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1.2 The Emergence of the Circular Economy Concept and Definitions The idea of the circular economy (CE) was first introduced by British environmental economists. They described the environment as a waste reservoir without a built-in propensity to recycle (Pearce and Turner 1990). In 1976, Stahel and Ready-Mulvey encouraged scholars to contribute to the natural resources and referred to CE as a “closed-loop economy”. Stahel and Reday-Mulvey (1981) In a regenerative ecology system, the origin of the word CE was not known. The term CE comes from western literature in the 1980s, to describe a closed-loop system concept to the recognition of the importance of quality of natural resources (Pearce and Turner 1990). In literature, the term gained prominence in the late ‘90s after prominence of the German and Swedish industrial ecological paradigm. CE was viewed as activity resource productivity and efficiency in the perspective of industrial ecology. Some claims that it first appears in China (Yuan et al. 2006). A circular economy is a multi-dimensional concept root in a product designed in such a way that can fully be recycled (Yap 2005). Several previous studies investigated CE definition in the engineering discipline based on the categorization of concepts in which CE appeared. CE received significant attention in the early 90s and now is considered a mainstream strategy for product design and social, economic and environmental sustainability. The concept of CE was narrow and the focus was on a healthy economy and environment co-existing as a way to integrate environmental management throughout the twentieth century (Chertow 2000). The German and Swedish environmental policy on a closed-loop originated from the industrial paradigm, and it has been followed by China’s policymakers as a prospective strategy to furnish a solution to existing environmental problems. The concept of CE comes out from the industrial ecology and has a closed-loop of product flow. Industrial ecology (IE) is referred Awan (2020) to as “a subdiscipline of the natural ecosystem which aims to restructure the industrial ecosystem in ways of managing and designing linear to closed-loop industrial production and consumption system. Industrial ecology seeks to form harmonized relations between ecological and human systems to provide sustainable benefits of all aspects of sustainability including social, environmental, and economic” (p. 370). The focus of CE is on eco-efficiency and resource productivity to achieving improvements in resources and achieve. The government of China that enforced the policies related to “circular economy”, these were referred to as “Circular Economy Promotion Law of the People’s Republic of China”. The concept of CE first to come into force in January 2009. CE was initially launched at three-levels in China, individual business level, eco-industrial parks, and at the eco-cities municipalities. Under these principles, CE aims to promote economic growth and at the same time bring about an economic development strategy (minimise material consumption and energy use) (Yuan et al. 2006). Industrial networks of China advance the initiative of CE and its practices at a country level. Another phenomenon related to macro-level or national level has been described as the promotion of consumption and production of efficient resources, promote

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eco-cities with the aim of developing a recycling-oriented society (Geng et al. 2012). The term micro level means implementing cleaner production initiatives at the firm level. Their term Meso level means the study of inter-firm at the supply chain level represented firms in cluster use waste of another firm (Yuan and Shi 2009). The word circular related to the concept of cycle and economy in terms of production and consumption of two cycles, (1) bio-geochemical cycles and (2) idea of recycling of products (Murray et al. 2017). The idea of the circular economy has been expressed by (Kirchherr et al. 2017b) in 3R (i.e., reduce, reuse and recover) and 4R (European Commission 2008), reduce, reuse, recycle and recover. CE is defined as “an economic model wherein planning, resourcing, procurement, production and reprocessing are designed and managed, as both process and output, to maximize ecosystem functioning and human well-being” (Murray et al. 2017). Yuan et al. (2006) concept of CE was the foundational step in developing CE as a path to achieving eco-efficiency and resource productivity. While existing research has in a common theme “cyclical closed-loop system” (Murray et al. 2017). The basic premises of the CE appear to be closing and slowing loops. Closing loops refers to (post-consumer waste recycling, slowing is about retention of the product value through 3R principals (Murray et al. 2017). Recently, Blomsma and Brennan (2017) term CE as prolonging resource productivity. However, Slowing resource loops referred as “Through the design of long-life goods and product-life extension (i.e. service loops to extend a product’s life, for instance through repair, remanufacturing), the utilization period of products is extended and/or intensified, resulting in a slowdown of the flow of resources”. Closing resource loops referred to as “through recycling, the loop between post-use and production is closed, resulting in a circular flow of resources” (Bocken et al. 2016, p. 309). Stahel (2016) referred good use of the utilization of end of life products as Loop (or circular) Economy. This unavailability of definitional comprehensibility brings forth the potential approach of misunderstanding, supporting dispersion than the convergence of views and additionally predicting cumulative examination of development on circular economy approaches (Table 1). It is interesting to note that no definitions define the stakeholder aspects except (Murray et al. 2017), but rather described what CE constitutes for human wellbeing. There are a variety of available definitions of CE and they are frequently referring to few dimensions, although they relate to different phrases, they fail to represent any guidance on how to manage stakeholders perspectives within these CE definitions. Circular economy order origin as a normative closed-loop strategy discipline becoming popular in 2003. Some scholars presented an inclusive treatment of CE definition which positions CE as a hybrid field intertwined from industrial ecology, natural resources and regenerative ecology system. Eco-effectiveness has been couched by CE to a great extent in which focus is to material extraction to continue use at the end of life and recycle. Literature analysis reveals that there is still a need to realise benefits from industry 4.0 including co-creation and expansion of the circular economy. Additionally, there are no research studies which investigated what artificial intelligence management capabilities needed for successful industry 4.0 transformations. Internet of things

Authors

Cooper (1999)

Yap (2005)

Yuan et al. (2006)

Geng and Doberstein (2008)

Liu et al. (2009)

Hu et al. (2011)

Bilitewski (2012)

MacArthur (2013)

Thomas and Birat (2013)

S. No.

1

2

3

4

5

6

7

8

9

Reuse of the materials

Focus on the closed flow of material

Emphasize on recycling

Reduction of use of energy and raw material consumption

Key terms

Use of waste

Eco-efficiency and resource productivity

“3R’s principles (Reduce, Reuse, Recycle) or Sustainable Design are concepts that should lead to this idea of a circular or closed-loop economy” (p. 5)

(continued)

Focus on closed-loop

“A circular economy is an industrial system that is restorative or regenerative by intention and design. It replaces Regenerative and the ‘end-of-life’ concept with restoration, shifts towards the use of renewable energy, eliminates the use of toxic restorative of resources chemicals, which impair reuse, and aims for the elimination of waste through the superior design of materials, products, systems, and, within this, business models” (p. 07)

“A circular system when the connection between resource use and waste residuals” (p. 1)

“Circular economy (CE) focuses on resource-productivity and eco-efficiency improvement in a comprehensive way, especially on the industrial structure optimization of new technology development and application, equipment renewal and management renovation” (p. 221)

“Circular economy defines its mission as solving the problems from the perspective of reducing the material flux Reduction of material use and making the material flow balanced between the ecosystem and the socioeconomic system” (p. 265)

“A circular economy approach encourages the organisation of economic activities with feedback processes which mimic natural ecosystems through a process of ‘natural resources → transformation into manufactured products → byproducts of manufacturing used as resources for other industries” (p. 232)

“CE is the circular (closed) flow of materials and the use of raw materials and energy through multiple phases” (p. 5)

“Circular economy is described as a scientific development model where resources become products, and the products are designed in such a way that they can be fully recycled” (p. 13)

“A circular economy is proposed, in which the throughput of energy and raw materials is reduced” (p. 10)

Key definition

Table 1 Representative definitions of circular economy

A Literature Analysis of Definitions for a Circular Economy 25

Geng et al. (2013)

Webster (2015)

Haas et al. (2015)

Sauvé et al. (2016)

Stahel (2016)

Murray et al. (2017)

Geissdoerfer et al. (2017)

Blomsma and “An emergent framing around waste and resource management that aims to offer an alternative to prevalent linear Take-make-dispose Brennan take-make-dispose practices by promoting the notion of waste and resource cycling” (p. 603) practices (2017)

Cullen (2017) “A circular economy is one that is restorative and regenerative by design and aims to keep products, components, Restorative and and materials at their highest utility and value at all times” (p. 483) regenerative by design

12

13

14

15

16

17

18

19

Key terms

Maximize ecosystem functioning

Resource effectiveness

Closed-loop of material flow

Closing economic and ecological loops

Restorative by design

Closing the loop for material

(continued)

“Circular economy as a regenerative system in which resource input and waste, emission, and energy leakage are Regenerative system minimised by slowing, closing, and narrowing material and energy loops. This can be achieved through long-lasting design, maintenance, repair, reuse, remanufacturing, refurbishing, and recycling” (p. 759)

“The circular economy is an economic model wherein planning, resourcing, procurement, production and reprocessing are designed and managed, as both process and output, to maximize ecosystem functioning and human well-being” (p. 377)

“Loop (or circular) economy is to bring goods and molecules back into new use in a grave-to-cradle approach” (p. 6)

“Production and consumption of goods through closed-loop material flows that internalize environmental externalities linked to virgin resource extraction and the generation of waste (including pollution)” (p. 49)

“The circular economy (CE) is a simple, but convincing, strategy, which aims at reducing both inputs of virgin materials and output of wastes by closing economic and ecological loops of resource flows” (p. 765)

“A circular economy is one that is restorative by design, and which aims to keep products, components and materials at their highest utility and value, at all times” (p. 16)

“A circular economy is an industrial system focused on closing the loop for material and energy flows and contributing to long-term sustainability” (p. 1256)

Reducing environmental damage

11

Key definition

Stahel (2013)

10

“Reducing the economic importance of resource extraction and waste management, and also reducing the environmental impairment caused by these industrial sectors” (p. 4)

Authors

S. No.

Table 1 (continued)

26 U. Awan et al.

Murray et al. (2017)

Zink and Geyer (2017)

Genovese et al. (2017)

Kirchherr et al. (2017b)

Moreau et al. (2017)

Homrich et al. “CE is a strategy that emerges to oppose the traditional open-ended system, aiming to face the challenge of (2018) resource scarcity and waste disposal in a win-win approach with economic and value perspective” (p. 534).

21

22

23

24

25

26

Key definition

Den Hollander et al. (2017)

20

Key terms

“A concept and practice, promoting closed material cycles by focusing on multiple strategies from material recycling to product reuse, as well as rethinking production and consumption chains toward increased resource efficiency” (p. 497)

“A circular economy describes an economic system that is based on business models which replace the ‘end-of-life’ concept with reducing, alternatively reusing, recycling and recovering materials in production/distribution and consumption processes, thus operating at the micro-level (products, companies, consumers), meso level (eco-industrial parks) and macro-level (city, region, nation and beyond), with the aim to accomplish sustainable development, which implies creating environmental quality, economic prosperity and social equity, to the benefit of current and future generations” (pp. 224–225)

“Circular economy pushes the frontiers of environmental sustainability by emphasising the idea of transforming products in such a way that there are workable relationships between ecological systems and economic growth” (p. 354)

“Circular economy-the concept of closing material loops to preserve products, parts, and materials in the industrial system and extract their maximum utility” (p. 1)

“An economy is envisaged as having no net effect on the environment; rather it restores any damage done in resource acquisition, while ensuring little waste is generated throughout the production process and in the life history of the product” (p. 371)

(continued)

Promoting resource resilience

Increased resource efficiency

Creating environmental quality

Restoration and value addition in resources

Closing material loops

Restoration by design

“The economic and environmental value of materials is preserved for as long as possible by keeping them in the Persevered material for economic system, either by lengthening the life of the products formed from them or by looping them back in the long-life system to be reused” (p. 517)

Authors

S. No.

Table 1 (continued)

A Literature Analysis of Definitions for a Circular Economy 27

Authors

Korhonen et al. (2018)

Suárez-Eiroa et al. (2019)

S. No.

27

28

Table 1 (continued)

Key definition

Key terms

“Circular economy is a regenerative production-consumption system that aims to maintain extraction rates of resources and generation rates of wastes and emissions under suitable values for planetary boundaries, through closing the system, reducing its size and maintaining the resource’s value as long as possible within the system, mainly leaning on design and education, and with a capacity to be implemented at any scale” (p. 14)

Regenerative production-consumption system

“Circular economy is an economy constructed from societal production-consumption systems that maximize the Maximizes the service service produced from the linear nature-society-nature material and energy throughput flow. Circular economy produced limits the throughput flow to a level that nature tolerates and utilises ecosystem cycles in economic cycles by respecting their natural reproduction rates” (p. 39)

28 U. Awan et al.

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applications in the CE are underutilised, and until now, there is a lack of research studies has been carried out regarding the implementation of Industry 4.0 in the resource conservation and closed-loop supply chain. CE as a regenerative concept inherently increases resource efficiency, effectiveness and encourage the continued use of the material as new strategic resource management. Literature shows that natural environmental system is changing as a response to activities in the last few decades. The purpose of CE is to develop a resource management strategy to achieve resource utilization at an optimal level and add value in the current consumption patterns. The circular economy is about continuous using products even after the end of life for downcycling or upcycling.

2 The Definition of Circular Economy Debate The exponential growth of the CE concept is evident in recent literature, however, uptake and use of CE as strategic management literature are still at infancy. According to Geng and Doberstein (2008) ecological modernization is a central concept in the circular economy. Kainuma and Tawara (2006) posited that environmental management is a sub-discipline of circular economy and include recycling, reuse concepts throughout the life cycle of services and products. Therefore, a key concern is the CE literature is what to consider in a traditional sense in an analysis of the definition. Rather, McArthur has extended this concept and highlight the need to keep materials in continuous use, rather eco-efficiency which focuses on dump the material at the end of life or recycled. CE views the firm as a resource effectiveness entity, accomplish straight objective to support the natural environment and meet the interest and expectations of the various players and full fill demands. Towards this end, CE is intended to address the key questions, which material is to use continuous and which material is to be used. As such, management attention to resources planning and management is at the heart of CE strategy (Bocken et al. 2017). Yuan and Shi (2009) and Zink and Geyer (2017) address CE as continuous use of the material and closing the loop. On the other hand, many authors explicitly address the multiple dimensions of the CE. In these papers (Cooper 1999; Liu et al. 2009; Stahel 2013) address the minimizing the environment impairment by reducing the material used. The remaining definitions, (MacArthur 2013; Webster 2015; Cullen 2017; Geissdoerfer et al. 2017; Murray et al. 2017; Suárez-Eiroa et al. 2019) focused exclusively on regenerative and restorative by design. Environmental and social issues were not solely characterized in any previous literature excepts (Geng et al. 2013; Kirchherr et al. 2017b; Murray et al. 2017). Blomsma and Brennan (2017) focused on the combination of taking make and dispose of and resource recycling. Den Hollander et al. (2017) highlighted the combination of environmental and economic ethics, and suggested that preserve material for the long-life. Genovese et al. (2017) focused on environmental sustainability by highlighting the importance of transforming a product that supports sustainable development consideration. Moreau et al. (2017) explicitly mentioned the importance of promoting closed

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material cycles and increase resource efficiency. Among the other CE characteristics examined, only the eco-efficiency and resource productivity were addressed by some of the definitions. None of the definitions was mentioned CE resilience as an adaptive ability to minimize environmental impairment and transforming products in such a notion of reusing and recovering material. The CE definition analysis revealed that so far, stakeholder perspective is not explicitly included in the definition, although stakeholder is considered to be part of a natural and ecology system. Stakeholder and resilience perspective is rarely discussed in the literature. This may help explain why these dimensions were not mentioned and incorporated in the CE published definitions. Based on the CE definition analysis, reduction of material use, ecoefficiency and resource productivity, regenerative and restorative of resources, focus on closed-loop, maximize ecosystem functioning, persevered material for long-life and promoting resource resilience are used randomly in the definitions. In essence, the circular economy is a set of practices aimed to keep products in its process after the end of life products. There are two main approaches, openly closedloop supply chain and closed-loop supply chain. A traditional re-manufacturing is concerned with economic value. Unlike the traditional recycling process, the recycling process in the circular perspective integrates the economic and environmental concerns. The literature on CE is growing as both practitioners and researchers begin to realise that the management of raw materials and end of life product is critically important to meet the needs of future generations. The characteristics of the circular economy(CE) identified in this paper provide the ground for proposing a new definition for CE. Building on the previous discussion in this paper, circular economy may be defined as: Circular Economy (CE) is an activity, set of process for reducing the material used in production and consumption, promoting material resilience, closing loops and exchange sustainability offering in such a way that maximize the ecological system.

Altogether, this shows that the purpose of the CE is increased material utilization and promotion of material resilience. Previous literature on CE has examined on meso, micro and macro level (Kirchherr et al. 2017b). Ecological system theory (Bronfenbrenner 1979) can be seen as an important theoretical lens to understand the CE. Currently, CE indicators are measured at three-level, micro, meso and macro level. Following (Bronfenbrenner 1979) ecological system theory dimensions, we extend it towards a better understanding of circular economy practices. A future research study with ecological system theory perspective may give further insights into our understanding of exo-system (firm does not experience and affect directly but indirectly by the external environmental forces, such as customers, competitors, buyers and social forces) and Chronosystem (is concerned with the environmental patterns influences on the firm circular economy practices over time). In exosystem, A firm does not experience and affect directly but indirectly by the external environmental forces, such as customers, competitors, buyers and social forces. In Chrono-system, a firm experienced and concerned with the continuous changing environmental patterns which influence on the firm circular economy practices over time.

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However, how CE is to be understood by the business and consumer marketing still poses a challenge. CE has started to make an impact on a different aspect of the business value chains and extends through the entire supply chain. CE has been interpreted in a variety of ways, ranging from resource reduction to resource transformation. In this situation, more investigation is needed to carefully evaluate what specific type of digitization tools could have a significant impact on the management decision in restorative and regenerative use of material. Early CE initiatives tend to focus on resource reduction, but increasingly they are focusing on the adoption of sustainable development objectives.

3 Conclusion In this chapter, the circular economy(CE) definition analysis is viewed from the manufacturing companies’ perspective utilising a critical literature review. It may not possible to develop a standard definition; however, it is possible to develop insights to expand the current literature on how CE is defined. The literature analysis showed that there were varied CE characteristics i.e., Reduction of virgin material, resource-productivity and eco-efficiency improvement, restorative or regenerative system, closed-loop, the value of materials is preserved and take-make-dispose practices. Circular Economy (CE) is an activity, set of process for reducing the material used in production and consumption, promoting material resilience, closing loops and exchange sustainability offering in such a way that maximize the ecological system. CE is diverging the application and use of natural resources rather than converging it. The concept of a CE is a value-orientated resource transformational process. CE considers both upstream and downstream production and consumption patterns to promotes the resilience orientation of resources. Given that research is still relatively new in the sustainable circular economy. CE offers a reverse resource regenerative idea to eliminate the linearity of production and consumption system to support sustainability objectives. CE is a tool of promotion of resilient sustainability objectives. CE is a new archetype for companies to achieve resource effectiveness and efficiency objectives by lowering their sustainability risks and impacts while raising material resilience. This chapter highlights the need that companies require new ways to define business models by incorporating eco-philia thinking. Circular economy business models set out pathways to provide opportunities for the dematerialization of resources throughout the product physical life cycle. CE is a practice of maximum utilization of material use across the whole life-cycle as well as deliver value addition in production and consumption. Literature addresses sustainability only in a few definitions, while resilience and stakeholder focus were not captured clearly by any of the published definitions. CE aims to integrate a system thinking approach as a way to achieve ecological efficiency and minimizing the environmental impairment into the material to support restorative and regenerative system and meets stakeholders’ requirements and improve organizational triple-bottom-line performance. A future research study with ecological system theory perspective may

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give further insights into our understanding of exo-system, Chronosystem in addition to meso, macro and micro level system. CE embedded in the internet of things (IoT) is not yet a discipline that has attracted attention in many emerging countries. CE practices in this context are particularly difficult to achieve in the manufacturing industry because the product shipped to various customers and difficult to keep track of the product.

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Reverse Logistics Flows and Network’s Configuration Problems

Robust Reverse Logistics Network Design Péter Egri, Balázs Dávid, Tamás Kis, and Miklós Krész

Abstract Recycling waste materials has become increasingly important recently both for economic and environmental reasons. In order to efficiently operate the backward flow of the materials, a basic challenge is to design the proper reverse logistics network containing the collection points, test centers and manufacturing plants. This paper studies the supply network of waste wood, which has to be collected in dedicated accumulation centers, and transported to processing facilities. We focus on the facility location of processing centers and propose mathematical models that take economies of scale and robustness into account, including a novel approach based on bilevel optimization. We also give a local and tabu search method for the solution of the problem. Test results are presented for both the robust and non-robust case using instances based on a real-life dataset. Keywords Facility location · Robust optimization · Economies of scale · Reverse logistics for wood recycling

P. Egri (B) Centre of Excellence in Production Informatics and Control, Institute for Computer Science and Control, Budapest, Hungary e-mail: [email protected] T. Kis Engineering and Management Intelligence Laboratory, Institute for Computer Science and Control, Budapest, Hungary Department of Operations Research, Eötvös Loránd University, Budapest, Hungary e-mail: [email protected] B. Dávid · M. Krész InnoRenew CoE and University of Primorska, Izola, Slovenia e-mail: [email protected] e-mail: [email protected] © Springer-Verlag GmbH Germany, part of Springer Nature 2020 P. Golinska-Dawson (ed.), Logistics Operations and Management for Recycling and Reuse, EcoProduction, https://doi.org/10.1007/978-3-642-33857-1_3

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1 Introduction and Motivation With the recent increase in the importance of environmental awareness, more stress is being put to on the end-of-life recovery and reuse of resources in supply chains. Activities that aim to recover resources from their final destination are integrated by the field of reverse logistics (Dekker et al. 2004). The goal of the reverse logistics is to use these end-of-life resources either to produce further value or to dispose of them properly, usually through a complex recovery process consisting of the stages of repair, reuse, refurbish, remanufacture, retrieve, recycle, incinerate and landfill. Reverse logistics methods can also be integrated into the conventional process of supply chains, forming so-called closed-loop supply chains that account for both forward and reverse flows of resources (Kazemi et al. 2019). Wood is an extremely versatile raw material with application fields ranging from paper production and packaging to the building industry. Moreover, wooden products can be reused and recycled after their original function becomes obsolete. According to data collected by the Horizon 2020 BioReg Project (Cocchi et al. 2019), the EU countries collectively produced between 40 and 60 million tonnes of yearly wood waste in the past ten years. Recovery rates of this depend on both the country and the type of wood waste, but it can be seen that there is room for improving the current amounts (Garcia and Hora 2017). The amount of research dealing with the management of waste wood has increased over the past years. As an example, the interest can be seen in the furnishing sector, where several studies have been conducted. The paper by (de Carvalho Araújo 2019) assesses the literature of circular economy in wood panel production. They conclude that while circular economy as a concept is being investigated with regard to waste production in this sector, mainly LCA (life cycle assessment) studies were carried out (Hossain and Poon 2018; Kim and Song 2014). Daian and Ozarska (2009) studied a sample group of six SMEs in the wood furniture sector of Australia and collected data about the current state of their wood waste and its reuse, recycle and disposal. Based on this, they formulated suggestions on wood waste management. Evaluating the availability of wood waste (and wood biomass in general) is also becoming more and more important, which can be seen from the multiple recent studies that have dealt with this question. Research by Verkerk et al. (2019) and Borzecki et al. (2018) assessed the potential availability of forest biomass from European forests and its spatial distribution, focusing on the hotspots of biomass. Studies comparing waste wood management in selected European countries were also conducted by Garcia and Hora (2017) and the BioReg Project (Cocchi et al. 2019). Although similar studies have become more widespread over the past years, the number of papers dealing with the mathematical modelling and optimization of processes in the waste wood supply chain is still scarce. Network design and planning is one of the most studied problem classes in logistics (Govindan et al. 2015). While there have been recent studies into the combined design of the network nodes and their possible links (Rahmaniani and Ghaderi 2013), it is usually safe to assume for transport problems that the underlying road network already exists. In this case, the

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most important problem to solve is facility location. The goal of this problem is to find an optimal placement of facilities on a network in order to minimize arising costs, which usually include transportation and opening facilities. Mathematical models of facility location are extensively studied, see e.g., Chap. 4 in Dekker et al. (2004). Further variations of the facility location problem (not specific to reverse logistics networks) can be found in Simchi-Levi et al. (2014). Stochastic variations of the problem can be found in Verter and Dincer (1992), which also considers capacity planning as the Capacity Expansion Problem once the facility locations are established. Dasci and Laporte study facility location and capacity acquisition by segmenting a market on the infinite continuous plain with uncertain demand (Dasci and Laporte 2005). In a recent manuscript, Ahmadi-Javid et al. study a combined facility location and capacity planning problem, where the facilities should serve customers with demand modeled as Poisson processes, which results in a nonlinear model (Ahmadi-Javid et al. 2018). Solution methods for facility location with economies of scales are studied in Bucci (2009), Lu (2010). Facility location problems usually consider two types of uncertainties; namely, stochastic parameters and disruptions (Chun et al. 2017). An example for the former one is the stochastic demand or cost parameters, see e.g., (Carrizosa and Nickel 2003). Robust models, on the other hand considers possible changes in the network structure, e.g., expected consequences of random disruptions or targeted attacks by malevolent attackers (Daskin 2013). Robust facility location is studied in Cheng et al. (2018). While general solutions designed for backward biomass streams have been studied in the past (e.g. Nunes et al. 2020; Sharma et al. 2013), we only found a handful of papers that focus entirely on waste wood. The reverse logistics network redesign problem for waste wood from the construction industry is investigated in Trochu et al. (2018), and a MILP (mixed integer linear programming model) was proposed for its solution. A use-case on a scenario from Quebec, Canada, was also presented. Devjak et al. (1994) formulated a mathematical model for optimizing the transportation of wood waste produced in sawmills, but did not present any computational experiments to back up its efficiency. Burnard et al. (2015) gave a reverse logistics model for facility location and transportation for waste wood, and presented computational results for a use-case in Slovenia. In this paper, we consider the facility location problem for transporting waste wood from accumulation centers to processing facilities. Besides transportation, we also study economies of scale as well as the robustness of the network in case of the breakdown of facilities. First, we formulate mathematical models for the problems, and propose both a local and tabu search heuristic method for their solution. The efficiency of these methods is shown on test instances generated using a real-life dataset.

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2 Problem Definition In the following subsections we formulate the uncapacitated facility location problem and its extensions.

2.1 Uncapacitated Facility Location Problem Let I denote the set of fixed accumulation point locations and J the set of potential facility locations. Let f j denote the cost of opening facility j and ci j denote the transportation cost from point i to facility j per m3 . Let u i denote the annual yield of waste wood from accumulation point i ∈ I (in m3 ). The formulation uses two types of binary variables: Y j is the indicator of opening facility j ∈ J , while X i j indicates product flow from accumulation point i to facility j. Note that due to uncapacitated facilities, an optimal solution always transports the whole amount of wood from each accumulation point to the closest open facility. The optimization problem is then the following binary integer problem:   f jYj + u i ci j X i j (1) min j∈J

i∈I j∈J

subject to 

Xi j = 1

∀i ∈ I

(2)

Xi j ≤ Y j Y j ∈ { 0, 1 }

∀i ∈ I, j ∈ J ∀j ∈ J

(3) (4)

X i j ∈ { 0, 1 }

∀i ∈ I, j ∈ J

(5)

j∈J

The objective function (1) minimizes the total opening and transportation cost, (2) ensures that the wood is transported from each accumulation point, while (3) states that all wood is transported to an open facility. Constraints (4) and (5) state that the variables are binary.

2.2 Economies of Scale Problem It is often realistic to assume that the higher the capacity of a facility, the lower its production cost due to the economies of scale (Garcia and Hora 2017). We consider the following production cost at facility j (based on Bucci 2009): S bj p j , where S j > 0 is the total quantity processed at facility j, p j is the unit production cost at facility

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j and b is a scale factor, typically −0.35 for manufacturing facilities and between −0.56 and −0.47 in the paper industry. With this modification the objective function of the program becomes non-linear as follows:    f jYj + u i X i j ci j + S bj p j S j (6) min j∈J

i∈I j∈J

j∈J

The constraints are the same as (2)–(5) with the following additional constraint defining the variable S j : 

X i j ui = S j

∀j ∈ J

(7)

i∈I

We still consider solutions where wood from each accumulation point is transported to only one facility, since there exist an optimal solution with this property, see Dupont (2008). However, it is no longer true that all wood should necessarily be transported to the closest open facility, for each set of open facilities an assignment problem should be solved to determine the optimal transportation.

2.3 Robust Optimization Problem Robust optimization can be modeled as a multi-objective optimization problem, where one objective is minimizing the cost in case of no disruptions, the other is minimizing the cost in case of a disruption. However, we consider only minimizing the cost in case of a disruption instead. More specifically, we consider a solution optimal, if any facility breaks down—i.e., all accumulation points connected with this facility must transport to another facilities—then the resulting cost in the worst case is minimal. We model this problem as a bilevel optimization: the leader determines which facilities to open, while the follower determines which accumulation point is connected to which facility. The follower’s problem assumes a given set of open and undisrupted facilities ({ j | Y j = 1 }) and assign the accumulation points to these facilities minimizing the transportation costs:  u i ci j X i j (8) min i∈I j∈J

subject to

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Xi j = 1

∀i ∈ I

(9)

X i j ≤ Y j

∀j ∈ J

(10)

∀i ∈ I, j ∈ J

(11)

j∈J

 i∈I

X i j ∈ { 0, 1 }

Note that the follower’s problem can be easily solved by transporting all the wood to the closest open facility. Let G(Y  ) denote the optimal objective value for the follower’s assignment problem on the input vector Y  . Then the leader’s problem is to determine the set of facilities to open with the minimal opening cost together with the transportation cost in case of the disruption of exactly one facility:

min Y

⎧ ⎨ ⎩

j∈J

⎧ ⎨

f j Y j + max G(Y  ) : Y ⎩

 j∈J

Y j + 1 =



Y j ∧ Y j ≤ Y j (∀ j ∈ J )

j∈J

⎫⎫ ⎬⎬ ⎭⎭

(12) This expresses that facilities { j | Y j = 1 } are opened, but then one of them cannot be used because of a disruption, therefore the transportation has to be determined not using the disrupted facility. The worst case is considered, i.e., when the disrupted facility causes the highest transportation costs. This corresponds to a pessimistic bilevel program.

3 Solution Approaches Solving facility location problems in realistic sizes (i.e., several thousands of accumulation points and possible facility locations) is computationally intractable even without considering economies of scale or robustness. Therefore, similarly to other works in this field, we used metaheuristic algorithms to find quasi-optimal solutions.

3.1 Determining the Worst Case Cost Effectively If economies of scale are disregarded, the optimal solution always transports the wood to the closest open facility. We use this observation to efficiently compute the cost in case of disturbances. Let πi denote a permutation of the facilities for each i such that ciπi1 < · · · < ciπin , where n = |J | is the number of facilities. If Y denote the status of the facilities with at least two open facilities, then let Fi (Y ) = min{Yπik = k

1} denote the closest open facility to point i, and let Bi (Y ) = min{Yπik = 1 ∧ k = k

Fi (Y )} denote the second closest one. If there is a disruption at facility Fi (Y ), then

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the wood from point i should be transported to facility Bi (Y ) instead, which means (ci Bi (Y ) − ci Fi (Y ) )u i additional transportation cost. Therefore in case of a disruption (ci Bi (Y ) − ci Fi (Y ) )u i . at an open facility j, the additional cost is CoD j (Y ) = i:Fi (Y )= j

Then the cost increase of disruption in the worst case is simply max CoD j . j:Y j =1

Therefore by maintaining the F, B and CoD vectors when the search heuristics open or close a facility, the value of the objective function can be determined efficiently.

3.2 Local Search Heuristic We use the neighborhood defined by Korupolu et al. (2000), which represents the solution only with the set of open facilities. Let S = { j | Y j = 1 } denote the set of open facilities, then the neighborhood of S is { T : |S \ T | ≤ 1 ∧ |S \ T | ≤ 1 }. From a solution S one can apply three operations to reach a neighbor: (i) open a new facility, (ii) close a facility (in case |S > 1|), and (iii) change the status of an open and a closed facility. This neighborhood contains O(|J |2 ) solutions, where J is the set of potential facilities. If one intends to solve the robust facility location problem, then instead of the cost defined by (1), the worst case cost should be considered.

3.3 Tabu Search Heuristic We have implemented the tabu search based on the approach described in Sun (2006) with some modifications. In addition to seeking the minimal cost in case of a disruption, we applied a different medium term memory process as well as different approach for updating the lengths of the tabu lists. The short term memory process is the following. Let k denote the number of moves since the start of the search and Δz kj the cost change by changing facility j’s status, i.e., closing if it is open and open if it is closed. The integer vector t is used to store the last time when the status of the facilities changed, i.e., t j is the value of k when facility j changed its status last. Let z 0 denote the best objective value in the current search cycle and k0 denote the time when z 0 was last updated. Let l0 (lc ) denote the tabu sizes for the open (closed) facilities, i.e., they cannot change status twice during this time interval unless the aspiration criterion is satisfied. These lengths are bound by lower limit lo1 (lc1 ) and upper limit lo2 (lc2 ). Each move is changing the status of a facility. We

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¯ where Δz k¯ = min{ Δz kj | facility j is not flagged }. A facility j¯ is choose facility j, j flagged, if the following tabu condition holds: k − t j¯ ≤ lc if Y j¯ = 0 or k − t j¯ ≤ l0 if Y j¯ = 1, but does not hold the following aspiration criteria: z + Δz kj < z 0 , where z represents the cost of the current solution. The short term process ends when the solution could not be improved for a specified time, i.e., when k − k0 > α1 n, where α1 is a parameter of the search. After each step the lengths of the tabu lists are updated: if the current solution improved the objective value, then l0 (lc ) is increased by one, otherwise it is decreased by one to extend the search space. In the medium term, we changed the frequency based memory process described by Sun (2006) and use a wider neighborhood instead. We seek for an open and a closed facility such that if we change their statuses, the total cost decreases the most. Sun states that considering this operation is costly, but our algorithm only use it when the short term process fails to improve the solution, thus providing a trade-off between computation time and solution quality. We have found that this approach performs better on the tested instances. If the solution can be improved, the search continues with the short term process. The medium term process ends when no improvement can be found. Finally, the long term process is invoked C times and when invoking the cth time, c moves are made changing the status of facility j¯ according to the following criterion: t j¯ = min{ t j | j = 1, . . . , n }.

3.4 Exact Solution of the Robust Problem The exact solution can be computed by completely enumerating all possible subsets of open facilities, and for each combination of open facilities, a simple assignment problem must be solved. However, we can apply the following simple bounding procedure to reduce the search space. When the algorithm already has a solution with objective value z, then for any subset of open facilities S where the sum of opening costs—disregarding the transportation costs—exceeds z, the search can ignore all supersets of S, since they cannot result in a less expensive solution. This exact method performs well if the opening costs are high compared to the transportation costs, because in those cases finding a good solution can restrict the search to a small number of open facilities. Nevertheless, this exact method works on small problem instances only.

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3.5 Bilevel Integer Program Formulation Considering the formulation of Sect. 2.3, it can be observed that once the Y  variables are fixed, the X variables are easy to determine to minimize the transportation costs by assigning each accumulation point to the closest open facility. This suggests that a solution of the following constraints determines an optimal assignment. X iπi1 ≥ Yπ i1 X iπik ≥ Yπ ik − 

k−1 

Yπ it

∀i ∈ I

(13)

∀i ∈ I, k = 2, . . . , m

(14)

∀i ∈ I

(15)

∀i ∈ I, j ∈ J

(16)

t=1

Xi j = 1

j∈J

X i j ∈ { 0, 1 }

Then, the inner maximization problem of (12) takes the form max



u i ci j X i j

(17)

i∈I j∈J

subject to (13)–(16) and the constraints  j∈J

Y j + 1 =



(18)

Yj

j∈J

Y j ≤ Y j

∀j ∈ J

(19)

Note that this formulation does not include non-linearity in contrast to the usual duality-based formulation (see e.g., Cheng et al. 2018). Using this observation, we search for the optimal solution where exactly k facilities (ρ1 < · · · < ρk ) are open: j∈J Y j = k and Yρl = 1 (l ∈ { 1, . . . , k }). Let Y l denote the vector that differs from Y only in its ρl th element and { X il j : i ∈ I, j ∈ J } the optimal transportation from accumulation point i to facility j using open factories determined by Y l . Then the optimization problem (12) becomes: min

 j∈J

subject to

f jYj + z

(20)

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P. Egri et al.



z≥

u i ci j X il j

∀l ∈ { 1, . . . , k }

(21)

∀i ∈ I, l ∈ { 1, . . . , k }

(22)

∀i ∈ I, s = 2, . . . , m, l ∈ { 1, . . . , k }

(23)

∀i ∈ I, l ∈ { 1, . . . , k }

(24)

∀ j ∈ J , l ∈ { 1, . . . , k }

(25)

∀j ∈ J

(26)

i∈I, j∈J l X iπ ≥ Yπl i1 i1 l ≥ Yπl is − X iπ is



s−1 

Yπl it

t=1

X il j = 1

j∈J

Y jl ≤ Y j k 

Y jl = (k − 1)Y j ,

l=1



Yj = k

(27)

j∈J

Y j ∈ { 0, 1 }

∀j ∈ J

(28)

∈ { 0, 1 }

∀ j ∈ J , l ∈ { 1, . . . , k }

(29)

∈ { 0, 1 }

∀i ∈ I, j ∈ J , l ∈ { 1, . . . , k }

(30)

Y jl X il j

Constraints (22)–(24) are the constraints of the inner optimization problem. Inequality (26) says that if Y j = 0, then all Y jl = 0, whereas if Y j = 1, then exactly k − 1 of the Y l has a 1 in position j. This, along with (25) implies that vectors Y l are all different, they are not bigger than Y (coordinate wise), and they have k − 1 coordinates of value 1, all other coordinates being 0. This formulation considers a fixed number of open facilities, therefore it should be solved for all possible (or realistic) k values.

4 Numerical Study Based on the industrial dataset of 1839 accumulation points and possible facility locations, we generated test sets containing 50, 100 and 500 locations, five different test cases for each set. Then we computed the solutions assuming different facility opening costs from the realistic 5 million to 1000. The solutions were computed using the local search, the tabu search, and when possible, the exact solver. For the tabu search we used the same parameters as (Sun 2006): lc1 = lo1 = 10, lc2 = lo2 = 20, C = 5 and α1 = 2.5. Table 1 contains the average results over the five test sets. The non-robust solutions aim at minimizing the total opening and transportation cost indicated in the cost column, while robust solutions aim at minimizing the worst case cost (WCC), i.e., the total opening cost and transportation costs in case of a facility disruption.

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We have estimated the production cost for the facility location model with the economies of scale, however, we have found that for realistic cases (large number of accumulation points, large facility opening costs, few open facilities) the economies of scale does not influence the solution. Therefore the non-linearity of the problem was not considered in the results presented below, which resulted in more tractable problems. The following indicators are included in the table: the cost of disruption (CoD) is the additional cost in case of a disturbance ((WCC-cost)/cost), the price of robustness (PoR) is the difference between the robust solution and the non-robust one ((robust cost—non-robust cost)/non-robust cost) and the benefit of robustness (BoR) is the difference in case of a disruption ((non-robust WCC—robust WCC)/non-robust WCC). This latter indicator cannot be interpreted when the non-robust solution contains only one opened facility, i.e., when in case of a disruption the whole network fails. The rows labelled with “OPT” denote the average costs of the optimal solutions. For the non-robust problem, it is computed by the the FICO XPRESS Solver using the formulation in Sect. 2.1, and for the robust problem the optimum is computed by solving the bilvel programming formulation of Sect. 3.5. Table 1 contains the results of the solutions considering 50 locations. The following observations were made: • For the opening costs between 1 million and 5 million, the exact solutions could be computed for the non-robust, and the robust variants as well, and both the local search and the tabu search could find the optimal solution in every case. • Changing the opening costs in a wide range (above one million) did not change the solutions. That means that the uncertainty of the exact opening cost does not matter too much. • For the 4 largest facility opening costs, the non-robust solutions contain only one opened facility, therefore they are quite vulnerable for disruptions. Adding one more factory to improve robustness is quite expensive, increasing the required budget by 36–76%. • Considering 1000 as the opening cost, the tabu search resulted in better solution both for robust and non-robust cases in one case out of five, therefore the last two rows are separate in order to differentiate the two approaches. The robust version of the problem could not be solved with the exact solver. • For an extremely low opening cost, large number of facilities are opened and even the non-robust solution offer some robustness. However, the robustness can be improved relatively inexpensively (for less than 0.4% of the budget) and in case of a disruption this can result in more than 10% saving in the additional costs. • In each cases, either the local search or the tabu search could find the optimal solution for the uncapacitated facility location problem without robustness. Table 2 contains the results of the solutions considering 100 locations. The following observations were made: • For opening costs 5 million and 2.5 million local search and tabu search resulted in the same results as the exact solver. The non-robust solutions in these cases always

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contain only one open facility and adding robustness by opening more facilities are quite costly. • For opening costs 1.6 and 1 million, the non-robust solutions contain one or two open facilities. The WCC and BoR values are the averages over the valid values. For these problems the solution of the local search and the tabu search often differ and it varies which performs better. • For opening cost 1000, the tabu search performed better in one case. It can be observed that increasing robustness in this case is quite inexpensive, but the achieved benefit is also lower that in the 50 facility case. • For only one problem instance neither the local search nor the tabu search heuristics could find the optimal solution for the uncapacitated facility location problem without robustness. Table 3 contains the results of the solutions considering 500 locations. The following observations were made: • With this size of solution space the result of the local search and the tabu search often differ and on average the tabu search performs slightly better. • Most of the non-robust solutions contain two or more open facilities. Optimizing for robustness increases the cost usually under 20%, therefore as the problem size growths, it becomes relatively less expensive to provide robustness. However, in case of the disruption robustness can save at least 10% of the additional cost, when the opening cost is above one million. • For five problem instances neither the local search nor the tabu search heuristics could find the optimal solution for the uncapacitated facility location problem without robustness. Four of these five cases have 1000 as the facility opening cost. As a conclusion it can be observed that including robustness is the most important when the number of opened facilities are low.

5 Conclusions In this paper, we studied the facility location problem in the reverse logistics network of waste wood. This network considered the accumulation centre for waste wood as well as the processing facilities where they have to be transported. The traditional facility location was extended with the consideration of economies of scale and robustness against the breakdown of facilities. We formulated mathematical models for these problems including a novel approach based on bilevel optimization, and also presented a local and tabu search heuristic method for their solution. We tested the efficiency of the proposed methods on instances generated using a real-life dataset. Different facility opening costs were considered, and robust and nonrobust solutions were examined in every case. While economies of scale seemed to have no influence on the solutions in the case of realistic cost parameters, robustness turned out to be significant when the number of opened facilities was low. In the case

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Table 1 Average performance using 50 locations Opening Non-robust Robust Open Cost 5000000 (OPT) 5000000 (LS/TS) 2500000 (OPT) 2500000 (LS/TS) 1666666 (OPT) 1666666 (LS/TS) 1000000 (OPT) 1000000 (LS/TS) 1000 (OPT) 1000 (LS) 1000 (TS)

WCC

1

6,452,010 –

2

CoD (%) 11,349,166 11,494,142 1.28

1

6,452,010 –

2

11,349,166 11,494,142 1.28

75.98

–

1

3,952,010 –

2

6,349,166 6,494,142 2.31

60.81

–

1

3,952,010 -

2

6,349,166 6,494,142 2.31

60.81

–

1

3,118,677 –

2

4,682,499 4,827,476 3.14

50.34

–

1

3,118,677 –

2

4,682,499 4,827,476 3.14

50.34

–

1

2,452,010 –

2

3,349,166 3,494,142 4.42

36.81

–

1

2,452,010 –

2

3,349,166 3,494,142 4.42

36.81

–

40.8 46,360

102,481

40.6 46,377 40.8 46,360

102,498 102,481

Open Cost

41.4 46,534 41.6 46,518

WCC

PoR (%) 75.98

BoR (%) –

93,605 93,588

102.20 0.34 102.21 0.34

10.57 10.58

of a larger number of opened facilities (which usually happened with unrealistically low facility costs) even the non-robust solutions contained some inherent robustness. While the heuristic method gave the same solutions for instances with a smaller number of locations (where they mostly found the optimal solution), the tabu search had a slight edge over the local search for larger instances. However, we were not able to obtain exact solutions for these instances with a large number of locations, and working on mathematical programming methods to help the solution of the model will be part of our future work. An important limitation of our model compared to other robust facility location models, is that we limit the possible number of disruptions to one. The reason behind this is that we are concerned with infrequent and random failures, instead of targeted attacks—which are not typical in the waste wood logistics. This limitation facilitates the novel bilevel integer program formulation, which can be solved more efficiently than general methods even for much larger problem instances, see e.g., (Cheng et al. 2018). However, it still cannot handle the huge networks typical in case of wood recycling. For these practical problems, we applied heuristics, and found that the tabu search using wider neighborhood in the medium term process yields significantly better results in reasonable time than frequency-based processes, e.g., (Sun 2006).

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Table 2 Average performance using 100 locations Opening Non-robust Robust Open Cost 5000000 (OPT) 5000000 (LS) 5000000 (TS) 2500000 (OPT) 2500000 (LS) 2500000 (TS) 1666666 (OPT) 1666666 (LS) 1666666 (TS) 1000000 (OPT) 1000000 (LS) 1000000 (TS) 1000 (OPT) 1000 (LS) 1000 (TS)

WCC

1

7,579,528 -

2

CoD (%) 12,383,253 12,594,382 1.72

1

7,579,528 -

2

12,383,253 12,594,382 1.72

63.58

–

1

7,579,528 -

2

12,383,253 12,594,382 1.72

63.58

–

1

5,079,528 -

2

7,383,253 7,594,382 2.90

45.67

–

1

5,079,528 -

2

7,383,253 7,594,382 2.90

45.67

–

1

5,079,528 -

2

7,383,253 7,594,382 2.90

45.67

–

1

4,246,195 –

2

1.2

4,327,338 8,102,164 2.2

5,703,829 6,083,148 6.33

32.85

5.87

1

4,246,195 -

2

5,612,645 5,998,162 6.67

32.95

–

1.4

3,510,617

2

1.6

3,554,200 5,663,822 2.2

4,237,162 4,616,481 8.76

19.82

15.35

1.4

3,533,251 6,062,458 2.2

4,242,211 4,628,458 8.85

20.56

17.01

0.18 0.32

4.05 4.07

77.6 90,535

118,734

77.6 90,535 77.6 90,535

118,734 118,734

Open Cost

WCC

PoR (%) 63.58

BoR (%) –

5,927,715

4,594,382

78.2 90,703 78.6 90,829

113,882 113,859

25.67 25.49

As a future work, we intend to further study the integer program formulation of the bilevel robust facility location model and use it for computing lower bound on the cost. In addition, we are going to examine the delivery planning problem in the network designed by the facility location optimization. Acknowledgements The research of Péter Egri and Tamás Kis has been supported by the National Research, Development and Innovation Office—NKFIH, grant no. SNN 129178, and ED_18-22018-0006. Tamás Kis was supported by Project ED-18-1-2019-030 (Application-specific highly reliable IT solutions), which has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme funding scheme. Balázs Dávid and Miklós Krész gratefully acknowledge the European Commission for funding the InnoRenew CoE project (Grant Agreement #739574) under the

Robust Reverse Logistics Network Design

51

Table 3 Average performance using 500 locations Opening Non-robust Robust Open Cost 5000000 (OPT) 5000000 (LS) 5000000 (TS) 2500000 (OPT) 2500000 (LS) 2500000 (TS) 1666666 (OPT) 1666666 (LS) 1666666 (TS) 1000000 (OPT) 1000000 (LS) 1000000 (TS) 1000 (OPT) 1000 (LS) 1000 (TS)

WCC

Open Cost

WCC

CoD (%)

PoR (%)

BoR (%)

1.6

19,242,002

2

19,497,959 31,080,579 2.4

23,068,454 25,278,632 9.79

1.6

19,307,357 33,319,075 2.4

23,062,160 25,421,665 10.31 19.78 20.13

2.4

14,263,693

2.2

14,372,353 25,267,673 3.8

16,318,181 19,023,002 17.26 13.69 22.33

2.6

14,304,504 21,116,645 3.8

16,336,745 18,676,174 14.55 14.38 11.35

3.2

11,900,321

3

11,952,078 18,065,126 4

13,464,150 15,561,839 15.60 12.69 13.48

3.4

11,905,914 17,393,652 4.8

13,380,271 15,323,262 14.60 12.36 11.43

4.4

9,496,951

4.4

9,496,951 13,729,015 6

10,402,176 11,881,160 14.26 9.60

12.61

4.4

9,522,457 13,678,493 5.8

10,429,309 11,946,057 14.65 9.48

11.80

282

396,016

281.4 396,159 282 396,054

454,042 453,937

281.60396,163 283 396,198

453,068 452,907

18.57 17.72

14.38 0.00 14.33 0.04

0.24 0.25

Horizon2020 Widespread-Teaming program, and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund). Miklós Krész is also grateful for the support of the Slovenian ARRS grant N1-0093. The authors would like to thank Aleksandar Tosic for his useful insights regarding the problem and for providing the real-world input dataset.

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Lu D (2010) Facility location with economies of scale and congestion. Unpublished master’s thesis. University of Waterloo Nunes L, Causer T, Ciolkosz D (2020) Biomass for energy: a review on supply chain management models. Renew Sustain Energy Rev 120:109658. https://doi.org/10.1016/j.rser.2019.109658 Peng C, Li J, Wang S (2017) Two-stage robust facility location problem with multiplicative uncertainties and disruptions. In 2017 international conference on service systems and service management, pp 1–6. https://doi.org/10.1109/ICSSSM.2017.7996131 Rahmaniani R, Ghaderi A (2013) A combined facility location and network design problem with multi-type of capacitated links. Appl Math Modell 37(9):6400–414. https://doi.org/10.1016/j. apm.2013.01.001 Sharma B, Ingalls R, Jones C, Khanchi A (2013) Biomass supply chain design and analysis: basis, overview, modeling, challenges, and future. Renew Sustain Energy Rev 24:608–627. https://doi. org/10.1016/j.rser.2013.03.049 Simchi-Levi D, Chen X, Bramel J (2014) The logic of logistics. theory, algorithms, and applications for logistics and supply chain management, 3rd edn. Springer Sun M (2006) Solving the uncapacitated facility location problem using tabu search. Comput Oper Res 33(9):2563–2589. Retrieved from http://www.sciencedirect.com/science/article/pii/ S0305054805002406 (Part Special Issue: Anniversary Focused Issue of Computers & Operations Research on Tabu Search) https://doi.org/10.1016/j.cor.2005.07.014 Trochu J, Chaabane A, Ouhimmou M (2018) Reverse logistics network redesign under uncertainty for wood waste in the crd industry. Resourc Conserv Recycl 128:32–47. https://doi.org/10.1016/ j.resconrec.2017.09.011 Verkerk PJ, Fitzgerald JB, Datta P, Dees M, Hengeveld GM, Lindner M, Zudin S (2019) Spatial distribution of the potential forest biomass availability in europe. Forest Ecosyst 6(1):5. https:// doi.org/10.1186/s40663-019-0163-5 Verter V, Dincer MC (1992) An integrated evaluation of facility location, capacity acquisition, and technology selection for designing global manufacturing strategies. Eur J Oper Res 60(1), 1–18. Retrieved from http://www.sciencedirect.com/science/article/pii/0377221792903287https://doi. org/10.1016/0377-2217(92)90328-7

Drivers and Barriers for Cooperation Between Municipalities in Area of Municipal Solid Waste Management Paulina Golinska-Dawson, Arkadiusz Kawa, and Piotr Januszewski

Abstract The growing amount of municipal solid wastes (MSW) per inhabitant is increasing in most of the European countries. The organization of efficient logistics for MSWs’ collection and recovery is crucial in order to improve the quality of environment and the human wellbeing. In European Union the municipalities are responsible to adopt measures to recycling and recovering of waste streams. The aim of this chapter is to identify the drivers and barriers inter-municipal solid waste. The data from over 230 Polish municipalities is analyzed. Our research contributes to the sustainability literature by providing the empirical evidence in this area. Keywords Inter-municipal cooperation · Solid waste management · Barriers and drivers

1 Introduction The European Union directive obliges member states to reduce their waste production and to adopt measures in order to increase the level of recycling of waste stream (EC 2008). The municipal solid waste (MSW) management must be organized in a way which is both environmentally and economically efficient. Previous research (e.g. Bel and Warner 2008; Bel and Mur 2009; Bel et al. 2013; Blaeschke 2014; Dijkgraaf and Gradus 2007; Warner and Hefetz 2003) have proved that collaboration between municipalities in the area of the MSW management can lead to more cost-efficient and environmental friendly solutions. Thus, such collaboration contributes to the fulfilment of the goals of sustainable policy by the reduction of waste being landfilled. P. Golinska-Dawson (B) Faculty of Engineering Management, Pozna´n University of Technology, Jacka Rychlewskiego 2 str., 60965 Pozna´n, Poland e-mail: [email protected] A. Kawa Łukasiewicz Research Network Institute of Logistics and Warehousing, Pozna´n, Poland P. Januszewski AtomScript, Pozna´n, Poland © Springer-Verlag GmbH Germany, part of Springer Nature 2020 P. Golinska-Dawson (ed.), Logistics Operations and Management for Recycling and Reuse, EcoProduction, https://doi.org/10.1007/978-3-642-33857-1_4

55

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Bel et al. (2013) have stated that there is a shortage of empirical papers analyzing the decision to engage in municipal cooperation. Our paper contributes to this research gap by providing empirical evidence from a big group of Polish municipalities. The aim of this chapter is first to examine the drivers and barriers of the collaboration among municipalities and then to propose a framework for facilitating decisions to engage in such a cooperation. In order to gain in-depth knowledge, we have designed and conducted national survey among Polish municipalities. This study seeks the answers for the research questions, as follows: • RQ1: Which factors (drivers) are encouraging the municipalities to collaborate by municipal solid waste management? • RQ2: Which factors (barriers) are making the collaboration by MSW management difficult? • RQ3: How can decisions to engage in such cooperation be facilitated? The paper is structured as follows. First a literature review is conducted in order to identify and classify the drivers and barriers. The survey‘s methodology is presented in Sect. 3. The results are analyzed in Sect. 4. The discussion on the empirical studies is provided in Sect. 5 and the framework is also presented there. Finally, the conclusions are stated in Sect. 6, including research limitations and future steps.

2 Literature Review The literature review has been conducted in three main areas, as presented in Fig. 1.

Fig. 1 Scope of the literature review

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57

2.1 Legal Regulation and Its Impact on the MSW Management The accession of Poland to the European Union in 2004 has stimulated a series of new environmental regulations with regard to MSW. The “Act on maintaining cleanliness and order in communes” from 1st July 2011 (updated on 19th July 2019) has introduced a new approach to MSW management based on the solutions used in other EU countries. The Act puts on the municipalities the obligations to organize a comprehensive MSW management. In accordance with that legal document the municipality may act as individual entity in the area of MSW management or establish a collaborative system with others. The main duties that must be fulfilled are, as follows: • to comprise all property owners in the commune in the MSW system, • to supervise collection of MSW from property owners, • to establish selective collection of MSW, including at least the following fractions: waste: paper, metal, plastic, glass and packaging and multi-material biodegradable municipal waste, biodegradable packaging, • to create points of selective collection of MSW in a way, that it provides easy access to all residents and where they can leave all waste electrical and electronic equipment (WEEE). Furthermore the municipalities should ensure the achievement of adequate levels of recycling, re-use and recovery. They are responsible to constantly reduce the weight of biodegradable MSW, which is transferred to disposal sites. The municipality has to provide the information and educational campaigns on the proper sorting of the MSW. The legal regulations are designed in a way which should ensure the continuous improvement of the MSW management at the local level. The legal regulations encourage the collaboration among the local governments in order to build up the regional infrastructure and lower the MSW management costs. Networks facilitate joint activities and enable the acquisition of resources, which are unavailable to any of the individual parties. The tasks of collaborative model for MSW management are, as follows: • construction, maintenance and operation of common installations and equipment for recovery or disposal of MSW, • organizing selective collection of MSW, • sorting and storage of MSW, • common promoting activities. In principle, members should jointly finance such investments. This is a favorable solution, especially for small and poor communities which are not able to independently carry out the activities. The cooperation might cover different activities, from segregation at the source, through collection of waste from households, to storage, recycling and/or disposal. Usually, such a cooperation also incudes a

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common pricing and educational policy. Collaborative MSW management system can develop common rules for waste collection and calculating related fees.

2.2 Municipal Solid Waste Management The literature on municipal solid waste management can be divided into three categories (Morrissey and Browne 2004): • cost benefit analysis, which enables decision-makers to see what scenarios are efficient in their use of resources, • based on life cycle assessment LCA, • based on multi-criteria decision making (MCDA), which takes into consideration several individual and often conflicting criteria leading to more robust decision making rather than optimizing a single dimensional objective function. Tanskanen (2000) has stated that the first MSW management models focused on optimization and dealt with specific aspects of that problem, for example vehicle routing. Current theoretical models reflect a change in governmental policy where MSW management is being transferred from a reliance on landfill, towards a wider range of recycling and recovery options. Integrated Solid Waste Management considers the full range of waste streams and selects the preferred recovery option based on site specific environmental and economic factors (Morrissey and Browne 2004). In our opinion the additional class of the literature on the municipal solid waste management can be distinguished, namely country-specific studies. These studies analyze different aspects of MSW at local (e.g. Bel and Mur 2009; Asase et al. 2009; Al-Jarallah and Aleisa 2014) or national level (Visvanathan and Trankler 2003). The literature on cooperation between municipalities by MSW management is very limited. Bel et al. (2013) has stated that the literature presents a shortage of empirical papers analyzing the decision taken to engage in cooperation. Warner and Hefetz (2003), as well as Bel and Mur (2008) have suggested that cooperation might be a good alternative to privatization, especially in smaller rural municipalities, where the amount of potential contractors is small. Municipal cooperation by MSW management in the Netherlands have been analyzed by Dijkgraaf and Gradus (2007). Bel and Mur (2013) have analyzed the effects of municipal cooperation and privatization on the costs of MSW management in rural areas. The results indicate that small towns that cooperate have lower costs for their MSW service, higher collection frequency and improved the quality of the service. Similar results has obtained by Blaeschke (2014). At the same time Sørensen (2007) has presented empirical results that those municipalities which cooperate have higher cost for the service than those municipalities that don’t cooperate. Garrone et al. (2010) have showed that in Italy responsible for managing municipal cooperation is a multi-government body, which may intensify leadership problems. They state that inter-municipal collaboration might suffer from

Drivers and Barriers for Cooperation Between …

59

higher coordination costs, which are not outweighed by the scale of achieved benefits. In their opinion the management tensions can be a significant source of inefficiency. Summarizing the literature review, it can be stated that the existing body of literature does not provide sufficient proof whether the cooperation by MSW management creates more benefits or problems. Our survey is contributing to that research gap by providing empirical evidence about benefits and problems in the inter-municipal cooperation with regard to MSW management. Due to the lack of the clearly defined in the literature drivers and barriers of intermunicipal collaboration by MSW management, the authors have applied in this work the “supply chain partnership” theory. In our opinion this theory is relevant, because the collaborative structure which municipalities create to manage MSW aim to collect, transport and reprocess waste from a big group of the citizens. The citizens create local decentralized “sourcing” networks from which streams of waste (materials) are transported to the centralized reprocessing facilities (for example recycling facilities, storage facilities, sorting facilities). The processes are also typical for supply chains (especially reverse supply chains).

2.3 Supply Chain Partnership Cooperation between independent entities can have a formal, as well as informal nature. Partnership in a supply chain should be understood as the formation of economic relations between its participants on the basis of trust, shared risks and benefits, leading to additional synergistic effects and a competitive advantage. In principle, this is significant and long-term cooperation which is defined as strategic partnership. It involves making joint business ventures aimed at achieving different individual objectives subordinated to common goals of the whole supply chain (Witkowski 2010). The concept of supply chain management assumes that all participants obtain benefits. Confrontation, ruthless competition and the “win-lose” market struggle become costly and ineffective and begin to give way to the prudent cooperative “win-win” game (Kempny 1998). At the same time, the “win-win” principle does not necessarily mean equal distribution of benefits; however, each of the partners should benefit from the cooperation. In case of reverse logistics processes the challenge is to reach the “win-win” situation, where both environmental and economic goals are met (Golinska and Kawa 2011). Parties involved in the cooperation have their own resources, capabilities, tasks, and objectives so there are difficulties in coordination of the constant flows of information, materials, and funds across multiple functional areas both within and between chain members (Golinska 2009). Building a partnership is an expensive undertaking - good communication, excellent coordination and an ability to share risk are needed. The partnership may be

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justified if the results achieved within its framework are better than the effects of individual business partners (Lambert et al. 2004). If the partnership is to be successful, it is necessary to be able to recognize the relations of great potential and together agree on the expectations concerning the partnership. One may wonder about the method for selection of partners, but sometimes it is easier to reduce the number of suppliers in order to be able to dominate these relationships. The idea is to organize the cooperation in such a way that the parties gain as much as possible. In order to do this, the expectations of the parties must be known, which is essential to build strong relationships. Leadership, goes beyond the organization, is associated with entrusting operating units with decisions, and includes negotiations with external partners and adjustment to constant changes (Schary and Skjøtt-Larsen 2001). At the top of the supply chain is a leader that integrates the spatially dispersed network entities, managing the flow of things, information and financial resources along the network. It determines the shape of the supply chain, choosing the right partners and mobilizing them to work, and protects it from competition from other chains. Govindan et al. (2013) have stated that coordination in the supply chain can be achieved by various means of coordination mechanisms used to motivate the members to participate in the optimization of the supply chain network. They are the four main coordination mechanisms in the supply chain presented in the literature (Govindan et al. 2013): contracts, information technology, information sharing, joint decision making. In case of inter-municipalities cooperation the focus is placed on coordination by contracts and joint decision making. Walker et al. (2008) have analyzed drivers and barriers to environmental supply chain management practices in the private and public sector. Some of their findings are also relevant to cooperation by the MSW management. They have identified the internal and external barriers for supply chain cooperation, as follows (Walker et al. 2008): • external: regulation, poor supplier commitment, industry specific barriers, • internal: implementation costs, lack of intimacy. In the same study are also identified the drivers, as follows (Walker et al. 2008): • external: regulation, customer, competition, society, suppliers, • internal: costs reduction pressure, organizational values. Other studies have identified the most important drivers for building relationships within business networks and cooperation, as follows (Ratajczak-Mrozek 2012; Sarmah et al. 2006): • • • •

new business opportunities, opportunities for growth, cost reduction, access to resources and capabilities, including knowledge, increasing the innovativeness of the company and its products,

Drivers and Barriers for Cooperation Between …

• • • •

61

reducing operational risk, increasing bargaining power against other entities, the benefits of specialization, economy of scale.

In our survey, when designing research questions we have decided to not distinguish the barriers into external or internal categories. The barriers are defined here, as follows: • regulations (mainly at local government level), • lack of suitable partners (as the equivalent to “poor supplier commitment”), • lack of cooperative structures in the region (as the equivalent to “industry specific barriers”), • higher costs of cooperation, • difficulties in a common realization of waste management tendering procedures (as the equivalent to “lack of intimacy”). The drivers are defined, as follows: • possibilities for cost reduction and economy of scale resulting in lowering the MSW fees for the inhabitants, • more efficient promotion and PR activities, • new possibilities for infrastructure investments by external financing (national and international funds), • organizational values (prestige), • increasing the bargaining power over companies which tender for waste management contracts. In the next section is presented the methodology for primary data collection in order to gain the in-depth knowledge for verifying the theoretical framework presented in Fig. 2.

3 Methodology for Conducting the Survey Due to the lack of sufficient secondary data to build up our model, we have decided to conduct the descriptive survey first, to collect the relevant primary data. The descriptive survey aims “understanding of the relevance of the phenomenon and describing the incidence or distribution of the phenomenon in the population” (Karlsson 2009). The survey aims to provide in-depth knowledge about the drivers and barriers and barriers to establish cooperation between municipalities for management of MSW. The following research steps have been conducted: 1. 2. 3. 4.

the municipalities database creation, construction of the research questions and its verification by academia experts, updating of the questionnaire and design of an on-line survey tool, survey distribution by emails among municipalities in Poland,

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New financing possiblities Cost reduction and economy of scale

Organizational values

More efficient promotion and PR

Increased barganing power

CMSWM

More difficult tendering procedure

Diffrent regulations

Higher costs of cooperation

Lack of suitable partners Lack of cooperative structures in the region

Fig. 2 Theoretical framework—drivers and barriers to establish cooperation between municipalities for management of MSW

5. data collection, 6. data analyses, 7. building the model and verification. The next subsections present in detailed the conducted research procedures.

3.1 Research Sample The local governance structure of Poland consists of three-levels: voivodships (regions), counties, and municipalities (also called communes). At present, Poland is divided into 16 voivodships, 380 counties, and 2479 municipalities (Karlsson 2009). There are four kinds of communes: • • • •

rural communes covering only rural areas, rural-urban communes which cover towns and rural areas, urban communes covering only towns/cities. cities with the district rights.

Drivers and Barriers for Cooperation Between …

63

Table 1 presents the population size in each region and the amount of and municipalities per region. The regions are shown in the decreasing order regarding their population size. Table 1 Characteristics of regions in Poland (elaborated based on data from stat.gov.pl) Voivodship

Population

[%] of population

Amount of communes

[%] of total communes

1

Masovian Voivodship (Mazowieckie)

5,301,760

13.8

314

12.7

2

Silesian Voivodship ´ askie) (Sl˛

4,615,870

12.0

167

6.7

3

Greater Poland Voivodship 3,462,196 (Wielkopolskie)

9.0

226

9.1

4

Lesser Poland Voivodship (Małopolskie)

3,354,077

8.7

182

7.3

5

Lower Silesian Voivodship 2,914,362 (Dolno´sl˛askie)

7.6

169

6.8

6

Lodz Voivodeship (Łódzkie)

2,524,651

6.6

177

7.1

7

Pomeranian Voivodeship (Pomorskie)

2,290,070

5.9

123

5.0

8

Lublin Voivodeship (Lubelskie)

2,165,651

5.6

213

8.6

9

Subcarpathian Voivodeship 2,129,951 (Podkarpackie)

5.5

160

6.5

10

Kuyavian-Pomeranian Voivodeship (Kujawsko-Pomorskie)

2,096,404

5.4

144

5.8

11

West Pomeranian Voivodeship (Zachodniopomorskie)

1,721,405

4.5

114

4.6

12

Warmian-Masurian Voivodeship (Warmi´nsko-Mazurskie) ´ etokrzyskie Swi˛ Voivodeship ´ etokrzyskie) (Swi˛

1,450,697

3.8

116

4.7

1,273,995

3.3

102

4.1

14

Podlaskie Voivodeship (Podlaskie)

1,198,690

3.1

118

4.8

15

Lubusz Voivodeship (Lubuskie)

1,023,317

2.7

83

3.3

16

Opole Voivodeship (Opolskie)

1,010,203

2.6

71

2.9

13

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The electronic survey was sent to 2459 communes in Poland. A combination of server and client side scripts has been used for the implementation of this customized e-survey. Emails containing a link to the questionnaire have been sent in 2 rounds two weeks apart. The overall response rate was 9.6%, what was satisfactory (because the sample N = 2459). We have received 136 responses for municipalities after the first round and 100 after the second attempt, which made a total of 236 valid responses. With a population of 2459, the minimal sample which should be taken into account for a survey is 208, assuming a 90% confidence level. Formula (1) was used to calculate this value. The number of received responses is higher than the minimal sample, therefore the authors have assumed that the survey results are valid. However all findings within this paper will be based on the above assumptions. The respondents have been divided into two groups: • IMSWM—individual municipal solid waste (MSW) managed by single municipality, • CMSWM—collaborative MSW management. n=

P(1 − P) e2 Z2

+

P(1−P) N

(1)

where: n—sample size P—assumed proportion of municipalities which are a member of a CMSWM (P = 0.3) e—error margin Z—the Z value which corresponds to the assumed confidence level (Z = 1645) N—population size.

3.2 Research Questions The questionnaire consists of 13 questions, which are structured, as presented in Fig. 3. The questions Q2–Q8 are addressed to the CMSWM. They focus on the verification of the drivers which have been identified based on the literature review and the previous in-field studies of the authors. Table 2 presents the scope of questions for the members of CMSWM’s systems. The questions Q9–Q13 are addressed to the non-members of the CMSWM’s system (see Table 3).

Drivers and Barriers for Cooperation Between …

65

Fig. 3 The logic of the research questionnaire (own elaboration)

Table 2 The scope of questions for members of CMSWM’s system (own elaboration) Questions

Scope

Q2

Does cooperation meet the requirements and help to fulfil the goals?

Q3–Q5

The pricing policy

Q6–Q8

Main benefits of being a member of a CMSWM

Table 3 The scope of questions for municipalities which are not member of CMSWM’s system (own elaboration) Questions

Scope

Q9

Previous experience with being a CMSWM member

Q10

Reasons for withdrawal from the CMSWM

Q11, Q12

Main barriers of being a member of a CMSWM

Q12, Q13

Main expected benefits of being a member of a CMSWM in the future

4 Research Results The responses have been received from municipalities located in all of the 16 Polish regions. Table 4 presents the response rates in each region. The responses in particular regions have been assessed as satisfactory (the mean value was 9.46% with standard deviation 1.9%), thus the national representation of the survey’s results has been ensured. Most of the communes (85%) have not yet established the collaborative structure in the area of MSW management. Pearson analysis has been undertaken to measure the correlation between the accession rate to CMSWM’s system and two most commonly used statistical indicators, namely: • the municipality area, and • population.

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Table 4 The response rates in each region (own elaboration) Voivodeship

Amount of responses

% of total response

% of response to total amount communes in region

1

Masovian Voivodeship

36

15.3

11.5

2

Silesian Voivodeship

19

8.1

11.4

3

Greater Poland Voivodeship

25

10.6

11.1

4

Lesser Poland Voivodeship

15

6.4

8.2

5

Lower Silesian Voivodeship

15

6.4

8.9

6

Łód´z Voivodeship

14

5.9

7.9

7

Pomeranian Voivodeship

14

5.9

11.4

8

Lublin Voivodeship

15

6.4

7.0

9

Subcarpathian Voivodeship

13

5.5

8.1

10

Kuyavian-Pomeranian Voivodeship

15

6.4

10.4

11

West Pomeranian Voivodeship

8

3.4

7.0

12

Warmian-Masurian Voivodeship ´ etokrzyskie Swi˛ Voivodeship

11

4.7

9.5

8

3.4

7.8

14

Podlaskie Voivodeship

11

4.7

9.3

15

Lubusz Voivodeship

11

4.7

13.3

16

Opole Voivodeship

6

2.5

8.5

236

100.0

13

Total

The results Pearson’s analysis has been shown in Table 5. The correlation between the size (area and population) of the municipality is relatively small. It is worth mentioning that both, small and large municipalities, are members of the CMSWM’s system in similar proportion. The authors have also tested a correlation between the type of a municipality and their decision to join a CMSWM’s system. In Table 6 is presented the data on the percentage of the municipalities who have joined a CMSWM (split by type). As can be observed most municipalities which have joined a CMSWM are classified as rural Table 5 Pearson analysis between CMSWM accession and the municipality’s population and area

Area in ha

Population

Area in ha

1

Population

0.319445263

1

Question 1

0.151359432

−0.040921992

Question 1

1

Drivers and Barriers for Cooperation Between …

67

Table 6 Comparison of the structure of municipality types Rural (%)

Urban (%)

Urban-Rural (%)

City with district rights (%)

Municipalities which have decided to join a CMSWM

47.06

14.71

35.29

2.94

Municipality’s structure in Poland

63.17

9.64

24.53

2.66

or urban-rural. However the comparison of the structure of municipality types and the percentage rates of municipalities which have decided to join a community is near equality. This means that there is no trend to join CMSWM’s by municipalities of a given type. To conclude, there has not been observed a clear correlation between the membership rate in CMSWM’s system and a municipality’s population, its area or its type. This means that the decision to join a CMSWM is based on other factors than the ones stated above. The respondents who are not members of any CMSWM’s system have been asked whether they would consider joining the inter-municipal cooperation in the future (e.g. next 2 years). The majority of the respondents 90.6% don’t want to build up the inter-municipal collaboration. The respondents that don’t belong to any CMSWM’s system have been asked also about barriers. The summary of their responses is presented in Fig. 4. About 45% of the respondents have stated that there is none collaborative structure in their neighborhood. About 33% of municipalities believe that their own IMSWM’s system is efficient and there is no need for cooperation in this area. About 13% Barriers for being a member of collaborative MSW management (CMSWM) No CMSWM in the area Sufficiently developed own WM system Lack of suitable partners Difficulties in joint tendering procedure for the waste collection &processing Differences in MSWM regulations between the muncipalities Other Higher WM costs 0% 5% 10%15%20%25%30%35%40%45%50%

Fig. 4 Barriers to join a CMSWM’s system

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of the respondents have stated that there lack of suitable partners for the creating of a new CMSWM’s system. The regulations on MSWM which differ between municipalities have been addressed as barrier by about 8% of respondents. Cost issues have been mentioned only by 6% of respondents. The respondents have also mentioned individually identified barriers named as “other” in Fig. 4. They are not discussed in this paper. The respondents who consider joining the CMSWM’s system have listed three potential benefits of such action, as: • easier access to EU subsidies, • lower cost of waste collection and processing/recovery, • lower cost of investment in infrastructure. The separate set of questions (Q2–Q8) have been directed to the communes who are members of CMSWM. Answering to the question Q2 about 86% of communes have declared that membership in CMSWM meets their requirements and needs. The respondents, who are members of a CMSWM have been asked (Q3–Q5) also about pricing policy and potential changes in the existing pricing practices. About 67% of communes use the same pricing policy within a CMSWM. That means that all the inhabitants within a particular CMSWM pay the same charge. The typical methods for calculating fees for MSW management are, as follows: • • • •

based on the number of residents, based on the surface of a property, based on the amount of water used, flat fee for every household.

About 80% of communes are calculating fee based on the number of residents per household. When determining that fee usually three types of costs are taken into account: the costs of waste management (collection and disposal of municipal waste, creation and maintenance of points of selective collection of municipal waste), the cost of transport and administration costs. About 78% municipalities don’t plan to change the pricing policy in the next two years. It can be assumed that the existing pricing policies are well designed. In questions Q6 and Q7 have been investigated the benefits of participating in CMSWM. The answers are presented in Fig. 5. These benefits can be also perceived as “internal and external drivers”, as referred in the previous literature studies. Only 11% of the communes haven’t identified any benefits of cooperation. Most of the municipalities have named the cost reduction as the main benefit. The cost reduction is usually resulting from the economy of scale and it allows lowering the waste collection fees for the inhabitants. The cost factor has been examined in detailed, and its elements have been identified as: • cost of waste processing, • cost of waste transportation.

Drivers and Barriers for Cooperation Between …

69

Drivers to be a member of a CMSWM More eficient promotion and PR

67%

Lower cost of waste collection & processing/recovery

58%

Easier infrastructure investments

44%

Simpler tendering procedures

36%

Lower waste transportation costs

33%

Easier access to EU subsidies

25%

None

11%

Presteige

11%

Other 0%

3%

10%

20%

30%

40%

50%

60%

70%

Fig. 5 Benefits of an inter-municipal cooperation by municipal solid waste management

About 44% of respondents have listed as the benefit also an easier access to resources and possibilities for new infrastructure investments. About 25% of the respondents have identified as benefit also new opportunities for external financing (national and EU funds).

5 Discussion The scope of collaboration by MSW management is still relatively low in Poland. The current policy aims to encourage this kind of collaboration. In our empirical survey we search for the answer for the following research questions: • RQ1: Which factors (drivers) are encouraging the municipalities to collaborate by municipal solid waste management? • RQ2: Which factors (barriers) are making the collaboration by MSW management difficult? • RQ3: How can decisions to engage in such cooperation be facilitated? The empirical results have shown that the cost reduction is the most important factor for creating inter-municipal collaboration. It is easier to achieve the economy of scale in a CMSWM. The costs of MSW collection, transportation, processing and recovery are lower in case of collaborative supply chain especially in case of small communes. These results are consistent with the previous research of Bel and Mur (2009), Bel et al. (2013). They have stated that by inter-municipal cooperation a cost reduction and the economy of scale can be achieved. Creating a CMSWM allows to lower the costs for the inhabitants in the area. The inhabitants are key stakeholders,

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so decreasing charges for them is an important goal of the municipality, whose governors are dependent on inhabitants (by direct election). Second important driver is the new possibility for external financing (national and international funds) in case of infrastructure investments. In our studies this driver is described by the two elements: • easier infrastructure investments, • easier access to EU subsidies. In Poland the EU development grants are an important source of financing of investments. So far the collaborative MSW management entities have easier access to apply for them. Third important driver, which has been identified in our survey is more efficient promotion and PR. The Polish regulations oblige the municipalities to promote selective collection of waste. The efficient information campaign also helps municipalities to solve problems with illegal dumping or avoiding the MSW sorting by the inhabitants. The analysis of the survey results shows that the increased bargaining power is the less significant factor. The least important factor is “organizational values” which has been only mentioned by 11.1% respondents. The second research question aims to examine the barriers for joining CMSWM. Our findings are consistent with the previous literature statements. The lack of established collaborative structure in the neighborhood or lacks of suitable partners are important barriers for development of a CMSWM in Poland. Furthermore a big group of communes believe that the already existing IMSWM’s infrastructure is sufficient and there is no need for cooperation. The non-parametrical statistics tests (chi square p values were insignificant) have showed that the other barriers are less influential. The difficulties in joint tendering procedure for the waste collection, processing and recovery are mentioned by some municipalities. The two least important barriers are potential higher cost of CMSWM and more complicated tendering procedures. However these barriers cannot be completely ignored. These results correspond with the findings of Garrone et al. (2010), who have stated that inter-municipal collaboration might suffer from higher coordination costs, due to the inefficiency of management. In order to find the answer for the last research question the results from our survey have been grouped into categories assigned to the model and normalized. The first step is to calculate the relative values of the drivers (ir ). We have assumed that the drivers should be maximized and we have used the formula from Kosacka et al. (2015). ir driver = where: id —is the recent value of the driver

i d − i dmin i dmax − i dmin

(2)

Drivers and Barriers for Cooperation Between …

71

idmin —is the minimum value for the driver idmax —is the maximum value of the driver. Then we repeat the procedure to calculate the relative states of barriers. We have made the assumption that barriers should be minimized. We have used the formula for relativization from Kosacka et al. (2015). irbarrier = 1 −

i b − i bmin i bmax − i bmin

(3)

where: ib —is the recent value of the barrier ibmin —is the minimal value for the barrier ibmax —is the maximum value of the barrier. Each driver has different importance level ηid , where 〖0 < η〗_id ≤ 1. The closer the value of ηid to 1, the more important that driver. Each barrier has different importance level ηid , where 〖0 < η〗_ib ≤ 1. The closer the value of ηib to 1, the more important is that barrier. The importance determinant is calculated separately for drivers and barriers from Formula 4 and 5. xid = ηid ∗ ir d

(4)

where: xid —is a importance determinant of ith driver xib = ηib ∗ ir b

(5)

xib —is a importance determinant of ith barrier. Table 7 presents the calculation of the importance determinants for drivers based on Formulas (2 and 4). Table 8 presents the calculation of the importance determinants for barriers based on Formulas (3 and 5). Table 7 Calculation of the importance determinants for drivers

Drivers

ηid

xid

Cost reduction and economy of scale (d1)

0.33

0.305533

New financing possibilities (d2)

0.27

0.185173

More efficient promotion and PR (d3)

0.20

0.13334

Increased bargaining power (d4)

0.13

0.048147

Organization values (d5)

0.07

0.007407

Total

0.6796

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Table 8 Calculation of the importance determinant for barriers Barriers

ηib

irbarrier

xib

No CMSWM in the area (b1)

0.29

0.565

0.1614

Sufficiently developed IMSWM’s system (b2)

0.24

0.675

0.1607

Lack of suitable partners (b3)

0.19

0.875

0.1667

Difficulties in joint tendering procedure (b4)

0.14

0.91

0.1300

Different regulations (b5)

0.10

0.925

0.0881

Higher cost of cooperation (b6)

0.05

0.945

0.0450

Total

0.7519

In Table 8 it was necessary to add one barrier which has not been initially identified in the preliminary theoretical framework (Fig. 3). For the respondents the own existing infrastructure was an important factor to not start the collaboration. At present the existing barriers are stronger than drivers to start cooperation. The “lack of existing structure” that the municipality might join, as well as “lack of suitable partners” to start a CMSWM from scratch are the important barriers. The empirical results have allowed us for verifying and modifying the preliminary framework presented in Fig. 3. The final version of the model is presented in Fig. 6.

Fig. 6 Framework to enable decision to join cooperation for MSW

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6 Conclusions The paper presents the issues related to inter-municipal collaboration by MSW management with focus on municipalities. On the example of Polish communes we have discussed the barriers and drivers of the inter-municipal collaboration. The empirical studies in this area are rather limited. Our paper contributes to the sustainability literature by providing empirical evidence from a relatively big group of municipalities in order to verify theoretical statements. Based on the theoretical review and the results from our survey the framework to enable decision to join cooperation for MSW has been created. The main findings of our paper are: • at present the existing barriers are stronger than drivers to start cooperation, • lack of existing structure that the municipality might joint is one of the main barriers, • lack of suitable partners to start a CMSWM from scratch is one of the main barriers, • existence of sufficient own infrastructure to manage MSW is very important blocking factor to start building up an inter-municipal collaboration, • the main driver for cooperation is cost reduction and possibility to achieve economy of scale, • the driver of high importance with the potential for enabling the future cooperation (helping to overcome main barriers) is “new financing possibilities” (thus, its importance determinant is higher than any of the three major barriers). The policy makers should develop the policy to encourage building up new inter-municipal cooperation by MSW management guiding the potential partners and providing networking tools. More external financing which is dedicated to the CMSWM should enable to establish cooperation. The analysis is not free of certain limitations which also suggest further areas for study. First of all, the analysis has included communes only in Poland, so the presented results might be country-specific biased. It might be also beneficial to go beyond the communes and to examine also other stakeholders in the CMSWM. For example public and private companies which perform the waste collection, transportation and processing tasks on behalf of the communes are very important stakeholders. Finally, due to the applied survey approach, more detailed analyses of particular relationships have been impossible to conduct. The in-depth studies should be conducted in the municipalities which belong to collaborative MSW management entities.

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References Al-Jarallah R, Aleisa E (2014) A baseline study characterizing the municipal solid waste in the State of Kuwait. Waste Manag 34(5):952–960 Asase M, Yanful EK, Mensah M, Stanford J, Amponsah S (2009) Comparison of municipal solid waste management systems in Canada and Ghana: a case study of the cities of London, Ontario, and Kumasi, Ghana. Waste Manag 29(10):2779–2786 Bel G, Mur M (2009) Inter-municipal cooperation, privatization and waste management costs: evidence from rural municipalities. Waste Manag 29(10):2772–2778 Bel G, Warner ME (2008) Does privatization of solid waste and water services reduce costs? A review of empirical studies. Resour Conserv Recycl 52:1337–1348 Bel G, Fageda X, Mur M (2013) Why do municipalities cooperate to provide local public services? An empirical analysis. Local Gov Stud 39(3):435–454 Blaeschke F (2014) What drives small municipalities to cooperate? Evidence from Hessian municipalities (No. 14-2014). Joint Discussion Paper Series in Economics Dijkgraaf E, Gradus RHJM (2007) Collusion in the Dutch waste collection market. Local Gov Stud 33:573–588 EC (2008) Directive 2008/98/EC of the European Parliament and of the Council on Waste Framework Directive 19 November 2008. http://ec.europa.eu/environment/waste/legislation/a. htm, Accessed 01.02.2014 Garrone P, Grilli L, Rousseau X (2010) Restructuring public enterprises: evidence from Italian municipal utilities. Available in SSRN: http://ssrn.com/abstract=1610124 Golinska P (2009) The concept of an agent-based system for planning of closed loop supplies in manufacturing system. In: Omatu S et al (eds) IWANN 2009 part II. LCNS vol 5518. Springer, Berlin, pp 346–349 Golinska P, Kawa A (2011) Recovery network arrangements WEEE case. In: Golinska P, Fertsch M, Marx Gomez J (eds) Information technologies in environmental engineering—trends and challenges, Springer, Berlin, pp 579–593 Govindan K, Popiuc MN, Diabat A (2013) Overview of coordination contracts within forward and reverse supply chains. J Clean Prod 47:319–334 Karlsson C (2009) Researching operations management. Taylor and Francis, New York Kempny D (1998) Co-markership: Zarz˛adzanie dostawami w biznesie przyszło´sci. In: Zarz˛adzanie ła´ncuchami dostaw. Conference proceedings. Katowice Kosacka M, Golinska-Dawson P, Mierzwiak R (2015) Sustainability classification for SMEs from the remanufacturing sector. Chiang Mai J Sci (in Press) Lambert DM, Knemeyer AM, Gardner JT (2004) Supply chain partnerships: model validation and implementation. J Bus Logist 25(2):21–42 Morrissey AJ, Browne J (2004) Waste management models and their application to sustainable waste management. Waste Manag 24(3):297–308 Ratajczak-Mrozek M (2012) Global business networks and cooperation within supply chain as a strategy for high-tech companies’ growth. J Entrepren Manag Innov 8(1):35–51 Sarmah SP, Acharya D, Goyal SK (2006) Buyer vendor coordination models in supply chain management. Eur J Oper Res 175(1):1–15 Schary PB, Skjøtt-Larsen T (2001) Managing the global supply chain. Handelshøjskolens forlag, Copenhagen Sørensen RJ (2007) Does dispersed public ownership impair efficiency? The case of refuse collection in Norway. Public Adm 85:1045–1058 Tanskanen J-H (2000) Strategic planning of municipal solid waste management. Resour Conserv Recycl 30:111–133 Visvanathan C, Trankler J (2003) Municipal solid waste management in Asia: a comparative analysis. In: Workshop on sustainable landfill management, pp 3–5

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Walker H, Di Sisto L, McBain D (2008) Drivers and barriers to environmental supply chain management practices: lessons from the public and private sectors. J Purchas Supply Manag 14(1):69–85 Warner ME, Hefetz A (2003) Rural–urban differences in privatization: limits to the competitive state. Environ Plan C: Gov Policy 21:703–718 Witkowski J (2010) Zarz˛adzanie ła´ncuchem dostaw: koncepcje, procedury, do´swiadczenia. Polskie Wydawnictwo Ekonomiczne, Warszawa

A Novel Formulation for the Sustainable Periodic Waste Collection Arc-Routing Problem: A Hybrid Multi-objective Optimization Algorithm Erfan Babaee Tirkolaee, Alireza Goli, Gerhard-Wilhelm Weber, and Katarzyna Szwedzka Abstract Municipal solid waste (MSW) management is among the essential tasks of municipalities that requires large amounts of fixed/variable and investment costs. In this system, the processes of collection and transportation include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are vital requirements for MSW management that need to be taken into account. In this study, a novel mixed-integer linear programming (MILP) model is developed to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to concurrently minimize the total cost and total environmental emission, and maximize citizenship satisfaction. To solve the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design method is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results demonstrate the high efficiency of the proposed model and algorithm to solve the problem.

E. B. Tirkolaee (B) Department of Industrial Engineering, Istinye University, Istanbul, Turkey e-mail: [email protected] A. Goli Department of Industrial Engineering, Yazd University, Yazd, Iran e-mail: [email protected] G.-W. Weber · K. Szwedzka Faculty of Engineering Management, Pozna´n University of Technology, Pozna´n, Poland e-mail: [email protected] K. Szwedzka e-mail: [email protected] G.-W. Weber Institute of Applied Mathematics, Middle East Technical University, Ankara, Turkey © Springer-Verlag GmbH Germany, part of Springer Nature 2020 P. Golinska-Dawson (ed.), Logistics Operations and Management for Recycling and Reuse, EcoProduction, https://doi.org/10.1007/978-3-642-33857-1_5

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Keywords Sustainable waste collection · Municipal solid waste · Periodic capacitated arc routing problem · Multi-objective invasive weed optimization algorithm · Multi-objective simulated annealing · Taguchi design method

1 Introduction Solid wastes generated in urban districts are known as municipal solid waste (MSW). There are various kinds of MSW that can be generated by residents, schools and universities, industrial companies, hospitals and clinical centers, public centers such as streets, markets, bus stops, parks, etc. The indisputable expansion of cities and the consequent increase of the population, particularly in recent years, has led to a growing consumption of materials. This has also resulted in an increase of waste generation in urban districts, such that in 2012, cities worldwide generated 1.3 billion tons of MSW that is equal to 1.2 kg per capita per day. Concerning these trends and figures, it is anticipated that the annual rate of waste generation reaches up to 2.2 billion tons by 2025 (Statista 2019). This issue is also highly perceptible in advanced countries. For example, in Western Europe, despite the successful training system, the annual generation of MSW per capita is over 600 kg (Gapminder 2019). As a result, the increased amount of waste generated in recent years requires an essential higher budget for the processes of collection and transportation. For example, the budget dedicated to MSW management in Toronto, Canada was $382.2 million in 2018 (Toronto City Council 2019). Furthermore, according to the reports by local organizations in Malaysia, more than $1 billion is paid for MSW management such that this amount constitutes almost 70–80% of the organizations’ incomes (Budhiarta et al. 2012). In this way, a strict and effective management is much needed to ensure the essential service level for establishing the requirements of public health and maintaining policies for the maximum amount of recycling/recovery. Unfortunately, based on the limited available resources, it is hard to establish a waste management system for municipalities which are in charge of the direct collection, transportation and disposal. Inefficient management and disposal of MSW lead to the pollution of water (groundwater and surface water resources), soil and air. In urban districts, lack of timely MSW collection results in the output of stagnant leachates in line with the growth of insects and the infection of diseases. Moreover, the emission of greenhouse gases (GHGs) associated with MSW transport has attracted much attention in recent decades, including in the European Union (EU) (Inghels et al. 2016). According to the European Environment Agency (EEA), waste collection and transportation accounts for up to 5% of direct GHG emissions, mainly due to short distances that MSW is shipped. However, following the adverse GHG emission growth associated with different modes of transport, this figure will rise to about 40% of net greenhouse gas emissions by 2020 (EEA 2008). Furthermore, job creation opportunity is worth considering in this field. Wastecollection vehicles need drivers and crew to perform the collection and transportation. Providing a sustainable waste collection and transportation system requires the study

A Novel Formulation for the Sustainable Periodic …

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of conflicts and trade-offs between economic, environmental and social objectives subject to the main operating constraints. Therefore, this research tries to investigate the sustainability aspect of MSW collection and transportation problem by designing an efficient methodology including mathematical model and solution techniques. Accordingly, a novel mixedinteger linear programming (MILP) model is developed to formulate the sustainable periodic capacitated arc routing problem (PCARP). The objectives are to minimize the total cost of the MSW management system, minimize the total pollution emission and maximize the total job opportunities. To solve the problem, a hybrid multiobjective optimization algorithm, namely, MOSA-MOIWOA is designed. Moreover, the Taguchi design method is employed to provide a higher efficiency for the proposed algorithm. The remaining sections of the paper are organized as follows. Section 2 provides a quick review on the background of the study. Section 3 explains the problem and the main assumptions of the model. Moreover, the proposed mathematical model is given in this section. Section 4 introduces the proposed solution techniques. The results and comparative analyses are given in Sect. 5. Finally, Sect. 6 provides the concluding remarks and suggestions for future studies.

2 Background The arc routing problem (ARP) is a kind of routing problem that has been specifically studied to formulate the vehicle routing operations in waste collection, snow removal of streets, street painting and other street-related services. This type of routing was firstly introduced by Golden and Wang (1981), which is a specific area of vehicle routing problem (VRP). Based on the literature, VRP is an extension of the traveling salesman problem (TSP) which was studied by a wide range of researchers, for example, see Lin and Kernighan (1973), Paletta and Triki (2004) and Lou et al. (2020). Furthermore, ARP is known as an extension of the Chinese Postman Problem (CPP) which can be found in many studies such as Pearn (1994), Filipiak et al. (2009) and Çodur and Yılmaz (2020). The most important research works on ARP and in the area of municipal solid waste management are summarized as follows. Bautista et al. (2008) modified the ARP by applying changes to the node routing to solve the waste collection problem in the municipality of a town of Barcelona. These changes were due to road constraints such as no U-turn to return from the edge. They solved the problem using the ACS algorithm, which was based on the nearest-neighborhood and closest-placement methods. Rodrigues and Soeiro Ferreira (2015) developed a mixed capacitated arc routing problem (CARP) for the collection and transportation of municipal waste. They considered a heterogeneous fleet of vehicles and multiple disposal sites in the proposed problem and solved the problem by CPLEX solver using benchmark problems. Babaee Tirkolaee et al. (2016) applied an SA algorithm to solve the robust CARP under fuzzy demand for urban waste collection. To improve the performance

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of the proposed algorithm, they employed the Taguchi design method to adjust the algorithm parameters and evaluated the performance of the algorithm compared to the CPLEX solver. A comprehensive review of ARP was conducted by Mourão and Pinto (2017) which considers the most important researches from 2010 to 2017. Tirkoaee et al. (2018) developed a robust CARP to solve the urban waste collection problem by considering driver and crew shifts. To solve the proposed problem and validate their mathematical model, they designed random examples and solved them by an exact method and SA algorithm. A hybrid genetic algorithm (GA) was developed by Tirkolaee et al. (2018) to solve the multi-trip capacitated green arc routing problem to collect MSW. To evaluate the performance of the proposed algorithm, they generated several random examples in different sizes and evaluated the results compared to the CPLEX solver. Tirkoaee et al. (2019) proposed a MILP formulation for the problem of multi-trip CARP for urban waste collection. To solve the problem, they developed an improved max-mini ant system (MMAS) algorithm and evaluated the performance of the algorithm compared to the heuristic algorithms and the simplified version of the max-min ant colony optimization algorithm by benchmark samples. Recently, several studies have been done in the area of uncertainty. A robust bi-objective MILP model was introduced by Babaee Tirkoaee et al. (2019) for multi-period multi-trip CARP under fuzzy demand. The model aimed to minimize the total cost and the longest travelled distance of vehicles simultaneously. They implemented multi-objective invasive weed optimization (MOIWO) algorithm and ε-constraint method to solve the problem. Babaee Tirkoaee et al. (2020) developed an improved hybrid ant colony optimization (ACO) algorithm to solve the multitrip CARP under fuzzy demand for urban waste collection. They implemented their proposed model as a case study in Isfahan, Iran. By scrutinizing the background, it is concluded that no research has been undertaken to study the economic, environmental and social aspects of urban waste management. Accordingly, a novel formulation is developed for the sustainable PCARP aimed at minimizing the total cost and total pollution emission, and maximizing the total job opportunity. Moreover, an efficient multi-objective algorithm is designed to solve the problem in large scales.

3 Problem Description This section introduces the sustainable PCARP and its specific characteristics. The operational objective is to determine the optimal number of required waste-collection vehicles among a fleet of homogeneous vehicles as well as constructing the optimal planning of routes. The sustainability is addressed by studying its three main aspects (economic, environmental and social) as separate objective functions in the proposed model. The first objective function seeks to minimize the total cost including the traversing costs and usage costs of vehicles. Total pollution emission is regarded as

A Novel Formulation for the Sustainable Periodic …

81

Fig. 1 Schematic view of the proposed problem

the second objective function to be minimized. The third objective function is to maximize job opportunities through increasing the number of hired labor. Accordingly, the sustainability aspect of the problem can be studied. The proposed network can be displayed by a graph G = (V, E), where V = {1, 2… n} denotes the set of nodes and E represents the set of edges. Each pair of nodes constitutes an edge of the network and each edge contains two arcs in opposite directions. Here, each edge in the set E may be a waste edge (required edge) or non-waste edge Thus, ER ⊆ E displays the set of required edges  (traversing edges).  so that ER = (i, j)|dijt > 0 , where dijt represents the demand of edge (i, j) in tth period. So, each non-waste edge has a demand equals to 0. Moreover, node number 1 stands for the depot and node number n shows the disposal site. Figure 1 presents a schematic view of the problem. As can be seen, the suggested MSW collection network in Fig. 1 is composed of seven required edges (1–2, 2–3, 3–4, 4–8, 5–6, 6–7, 7–8), one depot node and one disposal site. As a possible solution for a single planning period, all required edge are covered exactly by one vehicle and two trips. The required and traversing edges are specified by trash bins and dashed lines, respectively. The optimal routing for the first and second trips are 1–2 → 2–3 → 3–4 → 4–8 and 1–8 → 8–7 → 7–6 → 6–5 → 5–8, respectively. Finally, the vehicle moves back to the depot for completing its tour. The main assumptions of the model are given as follows: I. II. III.

Each required edge is covered only by one vehicle. Separate locations are considered for the depot and disposal site in the network. Vehicles begin their first trip from the depot and end it at the disposal site. Then start their possible 2nd, 3rd,…, pth trips from the disposal site and end at there again. IV. A fleet of homogeneous vehicles is taken into account. V. Vehicles have a maximum available service time. VI. Usage cost of vehicles include drivers’ and crew’s wage cost, fuel cost, hiring cost, etc., VII. A set of planning periods is regarded to serve the required edges.

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VIII. Environmental pollution is considered for the transportation of waste in urban districts. IX. Social aspect is studied by increasing the number of hired labor. Now, the main components of the proposed mathematical model including sets and indices, parameters and variables are listed as follows. Sets and indices V

Set of nodes; i, j ∈ V ,

E

Set of edges; (i, j) ∈ E,

ER

  Set of required edges; ER = (i, j)|dijt > 0 ,

K

Set of homogeneous vehicles; k ∈ K,

P

Set of vehicle trips; p ∈ P,

T

Set of planning periods; t ∈ T ,

S

Each possible subset of edges,

V [S]

Set of nodes constituting S,

Parameters cij

Distance of edge (i, j),

W

Available capacity for each vehicle,

dijt

Demand of edge (i, j) in tth period,

Tmax

Maximum available time for vehicles,

M

A large number,

tij

Traversing time of edge (i, j),

cvk

Usage cost of kth vehicle,

θ

Conversion factor of distance to cost

Gij

Amount of pollution emission released by traversing edge (i, j),

σ

Number of required workforce for each vehicle,

Decision variables xijkt

p

Number of traversing the edge (i, j) ∈ E by kth vehicle in pth trip and tth period,

p yijkt

1 if edge (i, j) ∈ ER is served by kth vehicle in pth trip and tth period, otherwise 0

ukt

1 if kth vehicle is employed in tth period, otherwise 0

p LTk p UTk

Total loading time of kth vehicle in pth trip and tth period, Total unloading time of kth vehicle in pth trip and tth period,

Now, the developed MILP formulation of the problem is as follows:

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⎛ minimize Z1 = θ ⎝

83

 

⎞ cij xijkt ⎠ + p

(i,j)∈E p∈P t∈T k∈K

cvk ukt

(1)

t∈T k∈K

 

minimize Z2 =



p

Gij xijkt

(2)

(i,j)∈E p∈P t∈T k∈K

maximize Z3 =



σ ukt

(3)

t∈T k∈K





p

xijkt =

i∈V [E]

p

xjikt ∀i ∈ V [E]; (i, j) ∈ E, ∀k ∈ K, ∀p ∈ P, ∀t ∈ T ,

(4)

j∈V [E]

  p p yijkt + yjikt = 1 ∀(i, j)or(j, i) ∈ ER , ∀t ∈ T ,

(5)

p∈P k∈K



p

dijt yijkt ≤ W ∀k ∈ K, ∀p ∈ P, ∀t ∈ T ,

(6)

p

(7)

(i,j)∈ER p

yijkt ≤ xijkt ∀(i, j) ∈ E, ∀k ∈ K, ∀p ∈ P, ∀t ∈ T ,  

p

xijk ≤ Mukt ∀k ∈ K, ∀t ∈ T ,

(8)

p∈P (i,j)∈E



p

LTkt = ul

p

dij yijkt ∀k ∈ K, ∀p ∈ P, ∀t ∈ T ,

(9)

(i,j)∈ER



p

UTkt = uu

p

dij yijk ∀k ∈ K, ∀p ∈ P, ∀t ∈ T ,

(10)

(i,j)∈ER



p

LTkt +

p∈P



p

UTkt +

p

tij xijkt ≤ Tmax ∀k ∈ K, ∀t ∈ T ,



p

xjhkt ≤ M

p

xijkt ∀S ⊆ E; {1, n} ∈ / V [S],

i∈V / [S],j∈V [S]

(j,h)∈S

∀k ∈ K, ∀p ∈ P, ∀t ∈ T , 

1 x1jkt ≥

j∈V [E]

 j∈V [E]

p

xnjkt ≥

(11)

p∈P (i,j)∈E

p∈P



 

 j∈V [E]

p+1

xnjkt



2 xnjkt ∀k ∈ K, ∀t ∈ T ,

(12) (13)

j∈V [E]

∀p ∈ {2, 3, . . . , |P| − 1}, ∀k ∈ K, ∀t ∈ T ,

(14)

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p

(15)

xjnkt = ukt ∀k ∈ K, ∀p = 1, ∀t ∈ T ,

p

(16)

xnjkt ≤ ukt ∀k ∈ K, ∀p ∈ P\{1}, ∀t ∈ T ,

p

(17)

p

(18)

x1jkt = ukt ∀k ∈ K, ∀p = 1, ∀t ∈ T ,

(1, j) ∈ E j ∈ V [E]\{1, n}  (j, n) ∈ E j ∈ V [E]\{1, n}  (n, j) ∈ E j ∈ V [E]\{1, n} 

xjnkt ≤ ukt ∀k ∈ K, ∀p ∈ P\{1}, ∀t ∈ T ,

(j, n) ∈ E j ∈ V [E]\{1, n} xijkt ∈ Z + , yijkt ∈ {0, 1}, ukt ∈ {0, 1} ∀(i, j) ∈ E, ∀k ∈ K, ∀p ∈ P, ∀t ∈ T , (19) p

p

p

p

LTkt , UTkt ≥ 0 ∀(i, j) ∈ E, ∀k ∈ K, ∀p ∈ P∀t ∈ T .

(20)

Objective function (1) minimizes the total waste collection and transportation cost which is composed of the traversing costs and usage costs of vehicles. Objective function (2) minimizes the total pollution emission released by vehicles. Objective function (3) maximized the number of hired labor in all periods. Constraint (4) represents the flow balance in the network for vehicles. Constraint (5) guarantees that required edges are served only by one vehicle. Constraint (6) shows the capacity limitation of vehicles. Constraint (7) states that the required edge can be served by the vehicle traversing it; i.e., vehicles may just traverse an edge without serving it. Constraint (8) indicates that vehicles can be employed if only their usage costs are paid. Constraints (9) and (10) calculate the sum of loading and unloading times for vehicles in each period and trip, respectively. Constraint (11) expresses the maximum available service time for vehicles in each period. Constraint (12) eliminates the potential sub-tours for vehicles in each period and trip. Constraints (13) and (14) form the sequence of vehicles’ trips from 1 to P. Constraints (15) and (16) guarantee that the first trip starts from the depot and ends at the disposal site, respectively. Constraints (17) and (18) indicate that if more than one trip is required, the next trips (second, third, fourth, etc.) start from the disposal site and end at there again. Constraints (19) and (20) display the domain of the variables.

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4 Methodology This section provides the proposed solution techniques to validate, solve and analyze the proposed problem. Accordingly, MOIWOA-MOSA is designed as a hybrid multiobjective optimization algorithm based on MOIWOA and MOSA. Moreover, the εconstraint method is applied to solve the model exactly using CPLEX solver/GAMS software.

4.1 Heuristic for Generating Initial Solutions In this section, a heuristic algorithm is developed to generate the initial solutions of the problem. The main implementation steps are given as follows: Step 1: At the starting point of the algorithm, choose the first planning period. Otherwise, if there is any planning period remaining, choose the next planning period among the existing periods and go to Step 2. Otherwise, go to Step 6. Step 2: Choose a vehicle randomly among the available vehicles to begin the first trip from the depot and go to Step 3. Step 3: Among the existing required edges, consider all the required edges that can be added to the trip based on the capacity and available service time limitations of the vehicle, then choose one edge with the minimum distance from the depot and go to Step 5. Otherwise, if there is no eligible required edge satisfying one of these limitations, move to the disposal site. Step 4: If the maximum available service time allows the vehicle to construct another trip, begin the next trip from the disposal site and go to Step 3, otherwise, go to Step 5. Step 5: If there is a remaining required edge to be served, go to Step 3. Otherwise, choose the shortest path to the disposal site and then go to Step 1. Step 6: Move to the depot and complete the tour. Step 7: Report the obtained solution.

4.2 MOSA Simulated annealing (SA) is a local search algorithm with a great ability to prevent from being trapped in local optima. This algorithm is very effective for solving nonconvex or discrete problems. Therefore, SA is employed to solve integer programming problems efficiently (Glover and Kochenberger 2006). From its introduction to the present, SA has demonstrated a high efficiency in solving large combinatorial optimization problems (Kubotani and Yoshimura 2003). Furthermore, the simplicity of implementation, convergence and hill-climbing for avoiding local optima are taken into account as the main superiority factors.

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Fig. 2 Pseudo-code of MOSA (Nam and Park 2000)

Accordingly, MOSA generates non-dominated solutions by using a simple probability function that tries to provide the solutions constituting the Pareto optimal front (Tirkolaee et al., 2020). Based on this probability function, the total space of objective is covered uniformly generating as many possible non-dominated and well-dispersed solutions. These features have made MOSA a fast reliable algorithm compared to the other existing multi-objective algorithms with a wide range of applications. The mechanism of the suggested MOSA is adapted from Nam and Park (2000). The superiority of MOSA against other EAs is that there is no need to employ high memory for keeping the population information. The pseudo-code of the suggested MOSA algorithm is given in Fig. 2. It should be noted that MOSA is applied to each initial solution generated by the heuristic algorithm in Sect. 4.1. Finally, we can provide a set of the high-quality initial solutions.

4.3 MOIWO The Invasive Weed Optimization algorithm (IWO) is an evolutionary and intelligent algorithm which was inspired by the processes of propagation, survival and weed adaptability. This algorithm was introduced by Mehrabian and Lucas (2006). Weed is a phenomenon that aims to provide the best environment for life and adapt to environmental conditions rapidly and at the same time, it is resistant to changes. At the beginning, the weed searches for a huge number of children, which can accordingly increase the quantity and available coverage of the environment (search behavior), then it continues to grow in competition with increasing quality to grow (greedy behavior) due to the capacity constraint. i.

The initial population (a given number of seeds) is generated and dispersed in the first stage. At the second stage, the dispersed seeds generate seeds themselves after growing and becoming a plant in terms of fitness and competence. In the third stage, these seeds (childs) are scattered and grow near its parent. Eventually, the second and third stages are repeated until the population reaches a given limit;

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ii.

iii. iv. v. vi.

87

otherwise, the remaining plants will continue to live better and the remainder will be ruined. The implementation of IWO has a suitable performance for all test functions. To simulate the colonizing behavior of weeds, the main principal features of the process are regarded as follows (Goli et al. 2019b): A certain number of seeds is spread over the search space. Each seed grows to a flowering plant and generates childs (seeds) with respect to the fitness value. The generated seeds are randomly dispersed over the search space and become new plants. This process is continued until the maximum number of plants is reached. Thereafter, only the plants with lower fitness values can survive and generate new seeds and the others are ruined. Now, this process continues until reaching the maximum number of iterations. Finally, the plant with the best fitness value is regarded as the nearest solution to the optimum.

Now, MOIWOA is a multi-objective variant of the IWO algorithm that was first introduced by Nikoofard et al. (2012) and has been extensively employed by researchers (Goli et al. 2019a, b). The execution steps of MOIWOA is given as follows: Step 1: Generating initial seeds The initial solutions are called initial seeds. These initial seeds are the output of the MOSA (Sect. 4.2) applied to the solutions generated by the heuristic algorithm (Sect. 4.1). Step 2: Seed reproduction In this phase, a member of the population is allowed to generate seeds regarding its own and colony’s lowest and highest fitness values according to Eq. (21): S = Smin + (Smax − Smin )

f − fworst . fbest − fworst

(21)

where S min and S max denote the minimum and maximum number of seeds, respectively. Furthermore, fworst and fbest represent the worst and best fitness value, respectively. After specifying the number of seeds, reproduction is performed by executing the following sub-steps: Sub-step 1: Exchange the routing plan for a given trip of the vehicle by another vehicle in a period. Sub-step 2: Select two different trips of two vehicles randomly. If there is a common edge/edges between these trips, select one of them randomly, then divide these trips into two parts. The first part of the first trip is combined with the second part of the second trip and the other two components of the trips are combined. Consequently, two new trips are generated.

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Sub-step 3: Select two trips of a vehicle randomly. If there are two common edges among them, exchange the sequences of the common edges. Sub-step 4: Select an edge in a trip of vehicle randomly and reverse its direction. Sub-step 5: Select a part of a trip randomly and reverse the direction of the included edges. Eventually, all the obtained solutions are checked in terms of feasibility and only the feasible solutions are kept in the population. Step 3: Competitive exclusion The number of plants will reach its maximum value by fast reproduction after several iterations, nevertheless, it is expected that the desired plants are re-generated more than the undesired ones. Moreover, it is critical to reach the maximum number of plants (pmax ) to efficiently handle the speed of MOIWOA (Goli et al. 2019a). In this stage, the solutions are sorted based on the non-dominate sorting technique that is described in the following. The weaker solutions are removed to keep pmax solutions for the next step. Step 4: Non-dominate sorting To rank a set of solutions, the following parameters are quantified for each solution: np as the number of solutions and S p as the set of solutions dominated by solution p. According to this technique, the value of crowding distances is the basic factor for sorting the solutions, which is calculated by Eq. (22):

dIjm = dIjm +



m Ij+1 fm fmmax



m Ij−1

− fm − fmmin



∀m.

(22)

where f mmax and fmmin stand for the maximum and minimum value of mth objective.

m Ij+1

m Ij−1

and fm show the value of mth objective for the next and previous Here, fm solution of jth solution according to the sorted value of mth objective function, respectively. Finally, the pseudo-code of the suggested MOIWOA is depicted in Fig. 3.

4.4 MOIWOA-MOSA The hybrid algorithm of this study works by generating high-quality initial solutions using MOSA to be incorporated into the MOIWOA. Finally, the best possible solution will be found by MOIWOA.

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Fig. 3 Pseudo-code of proposed MOIWOA (Goli et al. 2019a)

4.5 Taguchi Design Method As was clarified in the previous sections, MOSA and MOIWOA have several adjustable parameters that can affect the performance and final results. Hence, providing a suitable combination of these parameters can highly enhance the performance and efficiency of the algorithms. Two approaches for performing the test are known (Taguchi et al. 2005): (i) Standard analysis of variance (ANOVA) and (ii) Signal to noise (S/N) ratio. The amount of S/N indicates the amount of dispersion around a certain value, or how the obtained solutions have altered over different experiments. To reduce the dispersion of the objective functions as far as possible, S/N ratio plays an important role. This ratio stands for noise factors along with controllable parameters. By investigating among different Taguchi tables, the relevant table to L 27 is taken into account, which is implemented using Minitab statistical software. After applying the test on the input data in Tables 1 and 2, the obtained optimal values are given for MOSA and MOIWOA, respectively.

4.6 ε-Constraint Approach The ε-constraint approach is known as an effective way to deal with multi-objective optimization problems in the literature, which can generate Pareto solutions. Here,

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Table 1 Parameter of the MOSA algorithm Parameters

Initial values

Optimal values

1

2

3

Maximum number of iterations (A)

100

150

200

Initial temperature (B)

700

800

900

800

Temperature reduction rate (C)

0.9

0.95

0.99

0.99

Boltzman constant (D)

30

50

70

50

200

Table 2 Parameter of the MOIWOA Parameters

Initial values 1

Initial plant (N) Minimum seed (S min )

Optimal values 2

10

3

20

30

30

7

9

12

7

Maximum seed (S max )

100

150

200

200

Maximum plant (Pmax )

50

100

150

150

Maximum iteration (MI)

100

200

300

300

the Pareto front can be achieved by the ε-constraint approach based on Eq. (23) (Bérubé et al. 2009): minimizef1 (X ) subject to x ∈ X, f2 (X ) ≤ ε2 , ... fn (X ) ≤ εm .

(23)

The execution steps of the ε-constraint approach are given in Fig. 4. According to the offered ε-constraint approach, the first objective function is taken into account as the main objective function and the second and third objective functions as sub-objective functions. Now, the final formulation relates to the proposed model of the study is displayed in Eq. (24): minimizef1 (X ) subject to x ∈ X, f2 (X ) ≤ ε2 , f3 (X ) ≤ ε3 .

(24)

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Fig. 4 Execution steps of the proposed ε-constraint

5 Computational Results This section provides the model validation and comparisons between the proposed solution techniques through solving 10 problem instances in Babaee Tirkolaee et al. (2019). The parameters took value using a uniform distribution. For example, the demand parameter follows a uniform distribution uniform (1, 4). Moreover, the input information of these problem instances is represented by Table 3. The obtained results by the proposed solution techniques are shown in Table 4. With respect to the multi-objective essence of the suggested model, MOSAMOIWOA and the ε-constraint approach are compared using the mean of ideal distance (MID), number of solutions (NOS) and CPU time metrics. MID is employed to calculate the mean distance of the Pareto solutions from the ideal solution or the origin of the coordinates. Based on Eq. (25), the lower value of MID indicate more efficiency for the solution methods. Table 3 Information of the problem instances Problem

No. of nodes

No. of edges

No. of required edges

No. of periods

No. of vehicles

P1

7

13

8

1

2

P2

9

18

14

2

3

P3

12

28

20

3

4

P4

13

38

26

4

5

P5

14

45

33

5

6

P6

16

55

40

7

7

P7

18

75

54

10

8

P8

20

100

70

12

10

P9

22

150

110

15

12

P10

25

200

150

20

15

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Table 4 Comparison between MOIWOA and ε-constraint ε-constraint (EPC)

Problem

MOSA-MOIWOA

MID

NOS

CPU time

MID

NOS

P1

1029

7

P2

1179

14

P3

1797

P4 P5

49.3

1068

13

3.4

127.4

1259

24

5.6

25

289.9

1904

53

12.9

2468

29

724.3

2694

79

17.7

3079

30

1423.5

3122

127

23.6

P6

4849

30

2864.2

5091

136

31.5

P7

6168

34

3654.2

6418

150

42.5

P8

7285

35

5843.45

7380

150

53.9

P9

8193

39

10,000

8220

150

64.8

P10

–a

–

–

9793

150

79.3

Average

4002.22

27

2775.14

4695.49

103.2

33.52

a No

CPU time

solution found

1 N MID = s=1 NOS



3 m=1

2 fsol,m

(25)

CPU time value (s)

where fsol,m represents the mth objective value for the sth solution. Moreover, it should be noted that a run time limitation of 10,000 s is regarded to evaluate the performance of the solution techniques. Figures 5, 6 and 7 depict the comparisons for the metrics between solution techniques in different problems. As can be seen in Table 4, the ε-constraint approach couldn’t solve P10 within the run time limitation of 1000 s due to the complexity of the proposed problem in large sizes and the low efficiency of CPLEX solver to solve it. On the other hand, the suggested MOSA-MOIWOA solved the problem instances in less than 80 s. In fact, the comparison of the average CPU time values for these two solution techniques identifies that the MOSA-MOIWOA just spends about 1.2% of the CPU time required 7000 6000 5000 4000 3000

EPC

2000

MOSA-MOIWOA

1000 0 1

2

3

4

5

6

Problems

Fig. 5 Comparisons of CPU time values

7

8

9

10

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12000

MID value

10000 8000 6000

EPC

4000

MOSA-MOIWOA

2000 0 1

2

3

4

5

6

7

8

9

10

Problems

NOS value

Fig. 6 Comparisons of MID values 160 140 120 100 80 60 40 20 0

EPC MOSA-MOIWOA

1

2

3

4

5

6

7

8

9

10

Problems

Fig. 7 Comparisons of NOS values

for the ε-constraint approach. Figure 5 represents this comparison trends for the CPU time values. Moreover, by analyzing the values obtained for the two MID and NOS metrics, the quality of the final solutions can be evaluated. The lower MID and the more NOS reflect a more efficient solution technique. Figure 6 displays that these two solution techniques have a close behavior against each other in terms of MID value. Furthermore, the comparison of NOS values in Fig. 7 demonstrates that the MOSAMOIWOA has a relatively higher efficiency in this index.

5.1 Sensitivity Analysis This section provides a sensitivity analysis on P10 as the largest-sized problem that is the closest one to the real-life scale. For this purpose, the parameter of the maximum available service time (Tmax ) is studied under different change intervals and the behavior of the objective functions is evaluated using MOSA-MOIWOA. The obtained results are shown in Table 5 and Figs. 8, 9 and 10.

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Table 5 Obtained results for the sensitivity analysis Objective functions

Parameter’s change interval −20%

−10%

0%

+10%

+20%

Total cost

258,968.28

255,257.37

244,127.56

228,654.59

208,819.67

Total pollution emission

1735.35

1985.39

2115.80

2462.81

2533.73

Total job opportunity

379

368

351

324

311

Total cost value ($)

280000 260000 240000 220000 200000 180000 -20%

-10%

0%

10%

20%

Change interval

Total Pollution (kg)

Fig. 8 Sensitivity analysis of the 1st objective function 3000 2500 2000 1500 1000 -20%

-10%

0%

10%

20%

10%

20%

Change interval

Total Job opportunities

Fig. 9 Sensitivity analysis of the 2nd objective function 400 350 300 250 -20%

-10%

0%

Change interval

Fig. 10 Sensitivity analysis of the 3rd objective function

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Table 6 Change percentages of the objective functions against parameter’s change intervals Objective functions

Parameter’s change interval −20%

Total cost Total pollution emission Total job opportunity

−10%

0%

+10%

+20% −14.463

6.079

4.559

–

−6.338

−17.981

−6.164

–

16.401

19.753

7.977

4.843

–

−7.692

−11.396

As can be seen in Table 5 and Figs. 8, 9, 10, all objective functions reflect remarkable fluctuations against the changes of Tmax , but in various behavioral directions. Accordingly, the 1st and 3rd objective functions decreased by the increase of Tmax . This is due to the generated decrease in the usage costs of vehicles and this reduction leads to a significant decrease in the number of hired labor. However, the 2nd objective directly increased by the increase of Tmax as it was expected due to more usage of vehicles and less movement from the disposal site to the operational zone. Table 6 represent different change percentages of the objective functions against the four change intervals considered for Tmax . Accordingly, it is revealed that the most increase is related to the 2nd objective function; i.e., total pollution emission, where a 19.753% increase occurs for the 20% increase in Tmax . On the other hand, the most decrease is again related to this objective function, where a −17.981% decrease occurs for the 20% decrease in Tmax . Overall, increasing the parameter will improve the 1st and 3rd objective functions but it leads to a worse value for the 2nd objective function. Based on the obtained results, managers can investigate these trade-offs to find the optimal level of the resources to be provided and allocated in the waste collection system. In fact, sensitivity analysis is a useful tool to identify the optimal policy in a real-world situation.

6 Conclusions and Outlook This study designed an effective methodology to determine the optimal plans for waste-collection routes and the required number of vehicles. For this purpose, a novel MILP model was developed to formulate a multi-objective multi-trip sustainable PCARP. The objectives of the problem are to concurrently minimize the total cost, total pollution emission and total job opportunities, respectively. To validate the proposed mathematical model and cope with its tri-objectiveness, the ε-constraint approach was employed using CPLEX solver/GAMS software. Moreover, to solve the problem efficiently in large scales, a multi-objective meta-heuristic algorithm, namely, MOSA-MOIWOA was then developed. To improve the efficiency of the algorithm, the Taguchi design method was also applied to set the parameters. The performance of the MOSA-MOIWOA was then evaluated using various problem instances and three metrics of MID, NOS and CPU time in comparison with the

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ε-constraint method. The obtained results proved that the MOSA-MOIWOA can provide high-quality solutions within a much shorter CPU time. Moreover, the MID values were close to each other but the proposed algorithm could yield much better NOS value. Eventually, a sensitivity analysis was implemented on the maximum available service time of vehicles to assess the behavior of the objective functions and offer useful managerial insights and decision aids. It was obvious that the total cost and total job opportunity have indirect relations with this parameter, but the total pollution emission reflects a direct relation. According to the main limitations of the study, following suggestions are raised for future research directions: (i) Considering multiple depots and disposal sites within the network, (ii) Taking into account the locational decisions for depots and disposal sites, (iii) Extending the MSW management network by adding more facilities such as incinerators and recycling/recovery sites, (iv) Developing the social sustainability by minimizing the number of garbage pick-up to reduce social discomfort of odor presence time, (v) Studying the effect of milk-run strategy in the problem, (vi) Applying uncertainty techniques such as fuzzy theory, stochastic programming, grey systems and robust optimization, (vii) Employing other multi-objective algorithms like non-dominated sorting genetic algorithm III (NSGA-III).

References Babaee Tirkolaee E, Goli A, Pahlevan M, Malekalipour Kordestanizadeh R (2019) A robust biobjective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization. Waste Manage Res 37(11):1089–1101 Babaee Tirkolaee E, Mahdavi I, Seyyed Esfahani MM, Weber GW (2020) A hybrid augmented ant colony optimization for the multi-trip capacitated arc routing problem under fuzzy demands for urban solid waste management. Waste Manage Res 38(2):156–172 Bautista J, Fernández E, Pereira J (2008) Solving an urban waste collection problem using ants heuristics. Comput Oper Res 35:3020–3033 Bérubé JF, Gendreau M, Potvin JY (2009) An exact -constraint method for bi-objective combinatorial optimization problems: application to the Traveling Salesman Problem with Profits. Eur J Oper Res 194(1):39–50 Budhiarta I, Siwar C, Basri H (2012) Current status of municipal solid waste generation in Malaysia. Int J Adv Sci Eng Inf Technol 2(2):129–134 Çodur MK, Yılmaz M (2020) A time-dependent hierarchical Chinese postman problem. Central Eur J Oper Res 1–30 EEA E (2008) Better management of municipal waste will reduce greenhouse gas emissions. Support document to EEA Briefing, 1 Filipiak KA, Abdel-Malek L, Hsieh HN, Meegoda JN (2009) Optimization of municipal solid waste collection system: case study. Pract Periodical Hazard Toxic, Radioactive Waste Manag 13(3):210–216

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Gapminder (2019) United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. https://www.gapminder.org/data/documentation/gd003/. Accessed 15 Apr 2019 Glover FW, Kochenberger GA (eds) (2006) Handbook of metaheuristics, vol 57. Springer Science and Business Media Golden BL, Wong RT (1981) Capacitated arc routing problems. Networks 11(3):305–315 Goli A, Tirkolaee EB, Malmir B, Bian GB, Sangaiah AK (2019a) A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand. Computing 101(6):499–529 Goli A, Zare HK, Tavakkoli-Moghaddam R, Sadeghieh A (2019b) Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm. Numer Algebra, Control Optim 9(2):187 Inghels D, Dullaert W, Vigo D (2016) A service network design model for multimodal municipal solid waste transport. Eur J Oper Res 254(1):68–79 Kubotani H, Yoshimura K (2003) Performance evaluation of acceptance probability functions for multi-objective SA. Comput Oper Res 30(3):427–442 Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling-salesman problem. Oper Res 21(2):498–516 Lou CX, Shuai J, Luo L, Li H (2020) Optimal transportation planning of classified domestic garbage based on map distance. J Environ Manage 254:109781 Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inf 1(4):355–366 Mourão MC, Pinto LS (2017) An updated annotated bibliography on arc routing problems. Networks 70(3):144–194 Nam D, Park CH (2000) Multiobjective simulated annealing: a comparative study to evolutionary algorithms. Int J Fuzzy Syst 2(2):87–97 Nikoofard AH, Hajimirsadeghi H, Rahimi-Kian A, Lucas C (2012) Multiobjective invasive weed optimization: application to analysis of Pareto improvement models in electricity markets. Appl Soft Comput 12(1):100–112 Paletta G, Triki C (2004) Solving the asymmetric traveling salesman problem with periodic constraints. Netw Int J 44(1):31–37 Pearn WL (1994) Solvable cases of the k-person Chinese postman problem. Oper Res Lett 16(4):241–244 Rodrigues AM, Soeiro Ferreira J (2015) Waste collection routing—limited multiple landfills and heterogeneous fleet. Networks 65(2):155–165 Statista (2019) https://www.statista.com/markets/408/topic/435/waste-management/. Accessed 15 Apr 2019 Taguchi G, Chowdhury S, Wu Y (2005) Taguchi’s quality engineering handbook, vol 1736. Wiley, Hoboken, NJ Tirkolaee EB, Alinaghian M, Bakhshi Sasi M, Seyyed Esfahani MM (2016) Solving a robust capacitated arc routing problem using a hybrid simulated annealing algorithm: a waste collection application. J Ind Eng Manag Stud 3(1):61–76 Tirkolaee EB, Mahdavi I, Esfahani MMS (2018a) A robust periodic capacitated arc routing problem for urban waste collection considering drivers and crew’s working time. Waste Manag 76:138–146 Tirkolaee EB, Hosseinabadi A, Soltani M, Sangaiah A, Wang J (2018b) A hybrid genetic algorithm for multi-trip green capacitated arc routing problem in the scope of urban services. Sustainability 10(5):1366 Tirkolaee EB, Alinaghian M, Hosseinabadi AAR, Sasi MB, Sangaiah AK (2019) An improved ant colony optimization for the multi-trip capacitated arc routing problem. Comput Electr Eng 77:457–470 Toronto City Council (2019) City Budget. Report, City Government Toronto. Available at: https://www.toronto.ca/city-government/budgetfinances/city-budget/previous-budgets/2018citybudget/. Accessed 15 Apr 2019

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A Perishable Product Sustainable Supply Chain Network Design Problem with Lead Time and Customer Satisfaction using a Hybrid Whale-Genetic Algorithm Alireza Goli, Erfan Babaee Tirkolaee, and Gerhard-Wilhelm Weber Abstract In this research, designing a sustainable, multi-level, multi-products, and multi-period closed-loop supply chain network for perishable products is addressed. For this purpose, an integrated mathematical model is proposed. The main objectives are to minimize the production, distribution, and customer satisfaction related costs, minimize total CO2 emissions, and maximize social responsibility. The contributions of this research include considering lead time for production and delivering perishable products in the supply chain network design problem, and proposing a novel hybrid algorithm based on whale optimization algorithm (WOA) and genetic algorithm (GA). To solve the problem and optimize the mathematical model, the proposed hybrid algorithm is implemented on several test problems in different sizes. The obtained results are compared with augmented epsilon constraint in order to evaluate the performance of the proposed algorithm. The results indicate that the proposed algorithm provides Pareto solutions with acceptable quality and diversity. Keywords Closed-loop supply chain · Sustainable supply chain · Whale optimization algorithm · Genetic algorithm

1 Introduction Supply chain management consists of all movements as raw material storage, inventory in hand, and transportation of finished products from the source point to the A. Goli (B) Department of Industrial Engineering, Yazd University, Yazd, Iran e-mail: [email protected] E. B. Tirkolaee Department of Industrial Engineering, Istinye University, Istanbul, Turkey e-mail: [email protected] G.-W. Weber Faculty of Engineering Management, Pozna´n University of Technology, Pozna´n, Poland e-mail: [email protected] Institute of Applied Mathematics, Middle East Technical University, Ankara, Turkey © Springer-Verlag GmbH Germany, part of Springer Nature 2020 P. Golinska-Dawson (ed.), Logistics Operations and Management for Recycling and Reuse, EcoProduction, https://doi.org/10.1007/978-3-642-33857-1_6

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final consumption point. There are three strategic, tactical, operational, and operational levels in supply chain management. From the aspect of the supply chain design, determining the optimal location of facilities and the number of materials or products distributed between each facilitation in order to meet customers’ demands (Mardan et al. 2019; Burgess et al. 2017). Concerns have been raised about the effects of human works and human trafficking in recent years, and this has led to greater sustainability across the chain. Supply chain sustainability is one of the newest and most applicable concepts in supply chain management. Carter and Rogers (2008) presented a comprehensive conceptual framework for sustainable supply chain management. The overall dimensions of sustainability include economic, environmental, and social responsibility. The sustainable supply chain is a developed form of traditional supply chain management that tries to provide the economic, social, and environmental requirements of the supply chain through the flow of material between different levels. Sustainable supply chain management provides a competitive advantage for partners. In this regard, designing a sustainable supply chain network is a complex process in which all economic, social, and environmental aspects are used simultaneously (Mardani et al. 2020). Due to the importance of the presented topic, in this research, a sustainable multiproduct closed-loop supply chain network designed is addressed. The levels of the studied closed-loop chain include suppliers, manufacturers, distributors, customers, collection centers, and disposal centers. For this supply chain, all three dimensions of sustainability are optimized simultaneously. In other words, the objectives are to minimize total costs (economic objective), minimize total environmental pollution (environmental objective), and maximize total job opportunities (social objective). Moreover, customer satisfaction and delivery lead-time are considered. In this regard, a multi-objective mathematical model is proposed to find optimal sustainable closed-loop supply chain network. In order to optimize this model, a novel hybrid algorithm based on whale optimization algorithm (WOA) and genetic algorithm (GA) is proposed which is called HWOAGA.

2 Literature Review Babazadeh et al. (2017) have designed a biodiesel reproducible supply chain network. In their proposed model, minimizing the total cost of the biodiesel supply chain and minimizing its environmental impacts, is modeled under uncertainty in the form of a multi-objective contingency programming model. In the problem presented in this paper, strategic and operational decisions such as location decisions, allocation, facility deployment capacity, inventory turnover, etc. are determined under realistic assumptions. Zhuo and Wei (2017) presented a nonlinear integer for a location and inventory problem in a closed-loop supply chain. In this study, the quantity of demand is presented as an exponential relation of the price determined. To solve this model, an

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innovative heuristic method is applied to determine the inventory level and ordering period. Burgess et al. (2017), studied the integration of the forward and reverse logistics in order to design a closed-loop supply chain with considering demand uncertainty. For this purpose, in addition to minimizing the total costs, the reduction of CO2 emissions was also optimized. An ant colony algorithm has been developed to solve this model. Cao et al. (2018) proposed a mathematical model for a sustainable supply chain network design problem by considering perishable multi-products consumption. In this research, the authors presented tested their large-scale model using a genetic algorithm. Rad and Nahavandi (2018) presented an integrated multi-objective model to design a green closed-loop supply chain network considering the quality of products and discounts in the supply chain. The objectives of this model include reducing costs, reducing environmental pollution, and increasing customer satisfaction. The results of this study show that reducing environmental pollution will lead to good results in terms of customer satisfaction. Ghelichi et al. (2018) designed a green supply chain for the production of diesel cars. In this regard, a scenario-based multiobjective mathematical model is proposed and optimized with a stochastic programming approach. The objectives of this model include reducing costs and reducing environmental pollution. The epsilon constraint method has been developed to solve it under various scenarios. Liang and Quesada (2018) studied the fuel supply chain. They have identified and optimized the reduction of energy costs and greenhouse gas emissions as the most important goals of this supply chain. The research is being implemented in Japan and has provided an ideal solution that strikes a balance between energy costs and environmental pollution. Fard and Hajaghaei (2018) propose a three-level decision model to formulate the problem of designing a forward/reverse supply chain. They have used several meta-algorithms including taboo search (TS), variable neighborhood search (VNS), particle swarm optimization (PSO), and water wave algorithm (WWO) to solve their proposed model. Mardan et al. (2019) designed a multi-period and multi-product supply chain. In this model, two economic and environmental goals are examined. Initially, the two goals have become a goal comprehensively. Then they used the Bender analysis method to optimize it. The results show that the new model consumes 13% of total chain costs. Niranjan et al. (2019) investigated and optimized a multi-channel closed-loop supply chain. In this supply chain, the coverage radius is intended to meet customer demand. This supply chain is optimized under the two objectives of total cost and total supply chain contamination. The optimized CLPEX solver is used. The results indicate the efficiency of the chain structure studied.

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Yavari and Grally (2019) proposed a closed-loop green supply chain optimization model under uncertainty. In this model demand is uncertain and a robust optimization approach is used to meet it. In this chain the products are perishable. Two objectives of total cost and total pollution are optimized in this chain. A new innovative method called YAG has been used for this optimization. The results show a 1.65% improvement in this method over other methods. Goli et al. (2019) designed a closed-loop supply chain network with financial flow consideration. In this research, a twoobjective mathematical model is presented. The first is to increase net supply chain assets and the second is to increase job creation as the supply chain social responsibility. The Epsilon constraint method and gray wolf optimization (GWO) are used to design this supply chain network. The results show that taking into account the financial flow can bring more profits to the whole supply chain. Recently, Rabbani et al. (2020) proposed a new mathematical model for locationallocation in a sustainable supply chain. In this research, different technology levels for the transportation fleet are considered. Moreover, Hybrid Robust Possibilistic Programming, is applied and a case study is solved by the improved augmented epsilon constraint and some sensitivity analysis is performed. Finally, the most important contributed researches are reviewed in Table 1. Based on the articles reviewed in Table 1, it is clear that in previous research, several methods have been used to optimize the supply chain network design problem. Among the methods used, the genetic algorithm has been presented as an efficient method. However, none of them used the WOA which is one of the novel metaheuristic algorithms. Therefore, the main innovation of this research can be considered in the development of a new meta-heuristic algorithm based on hybrid whale optimization and genetic algorithms to solve the closed-loop supply chain network design problem.

3 Proposed Mathematical Model In this research, a sustainable supply chain for perishable products is investigated. Accordingly, economic, environmental, and social aspects are considered as independent objective functions. In the studied supply chain, essential raw materials are supplied from different suppliers. After production, products are sent to different distribution centers (DCs) and they store them after inspection and distribute at appropriate times between customers. The returned products are shipped to the hybrid collection and disposing centers. The reusable products are shipped to production plants and the remaining are disposed. The structure of this supply chain is shown in Fig. 1. The distributor has the ability to store goods and deliver in the coming periods. This condition causes a percentage of products in the distributor’s warehouse to be corrupted and needs to be returned to the plants. The manufacturing units process the returned products from distribution centers and hybrid centers, then return them

2017

2017

Babazadeh et al. (2017)

Zhuo and Wei (2017)

2018

2018

2018

2019

Rad and Nahavandi (2018)

Ghelichi et al. (2018)

Liang and Quesada (2018)

Fard and Hajiaghaeii (2018)

Niranjan et al. (2019)

2019

Mardan et al. 2019 (2019)

2017

Cao et al. (2018)

Burgess et al. 2017 (2017)

Year

Author(s)

√

√

√

√

√

√

√

√

Allocation

Location

√

√

√

√

√

√

√

√

√

√

√

√

√

√

Multi period

√

√

Multi-commodity

√

√

√

Reverse logistic √

√

√

√

Forward logistic

Table 1 Brief review and comparison of the literature

√

√

√

√

√

√

√

√

Environmental aspect √

√

√

Social aspect

(continued)

Exact solution

Benders decomposition

VNS-PSO-WWO

Exact solution

Epsilon constraint

Exact solution

Genetic algorithm

Ant colony algorithm

Heuristic

Stochastic programing

Methodology

A Perishable Product Sustainable Supply Chain Network … 103

Year

2019

2019

2020

2020

Author(s)

Yavari and Geraeli (2019)

Goli et al. (2019)

Rabbani et al. (2020)

This research

Table 1 (continued)

√

√

√

Allocation

√

√

√

Location

√

√

Forward logistic √

√

√

Reverse logistic √

√

Multi-commodity

√

√

Multi period

√

√

Environmental aspect √

√

√

√

Social aspect

Hybrid WOA-GA

augmented epsilon -constraint

GWO

YAG

Methodology

104 A. Goli et al.

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105

Fig. 1 Structure of studied supply chain

to the distribution to be sold to customers. Other returned products are considered as waste products and are disposed. In order to meet the demand of each customer in a period, the supply chain should provide this amount in specified lead time. Moreover, customer satisfaction is determined by the rare of receiving products in each period. Customers need to get their products at a fixed rate and the fluctuation of receiving products has a distinct cost to the supply chain. Other assumptions of the mathematical model are as follows: I.

The supply chain has 5 main levels, including supplier, manufacturer, distribution centers, customers, and hybrid collection and disposing centers. II. Different transport systems are considered each level of the supply chain with fixed and variable costs. III. Only one transportation system can be used between each supplier and distributor, as well as between each distributor and customer. IV. The supply chain is required to meet the total demand of customers in specified lead time. V. There are several potential centers for the establishment of distribution centers, one or more of which must be established. VI. The number of facilities to be established is not known beforehand, but the mathematical model should provide the best possible scenarios, given the amount of demand and establish costs. VII. The transportation system selection is used for sending final products from plants to DCs and also return products from DCs to plants. Now, the main components of the proposed mathematical model including sets and indices, parameters and variables are listed as follows. Indices S

Set of suppliers fixed locations (s = 1, 2, …, S),

I

Set of plants fixed locations (i = 1, 2, …, I),

J

Set of potential locations for distribution centers (j = 1, 2, …, J),

C

Set of customers fixed locations (c = 1, 2, …, C), (continued)

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(continued) D

Set of hybrid collection and dispose fixed locations (d = 1, 2, …, D),

P

Set of products (p = 1, 2, …, P),

R

Set of raw materials (r = 1, 2, …, R),

L

Set of transportation systems (l = 1, 2, …, L),

T

Set of time period index (t = 1, 2, …, T ).

Parameters t Dcp

Demand of customer c for product p in period t,

t RCcp

Rate of returning product p from customer c in period t to hybrid collection/dispose centers,

RHpt

Rate of returning product p from hybrid collection/dispose centers to plants in period t,

LTcp

Lead time to deliver customer c demand for product p,

t SCsr t MCip t ICjp

Unit purchasing cost of raw material r from supplier s in period t,

t HCjP

Unit maintenance cost of product p at distribution center j in period t,

FYjt t CSsr CXit CYjt t CSIsir t CIJijpl

Fixed cost of establishing the distribution center j in period t,

t CJCjcpl

Unit transportation cost of product p from distribution center j to customer c with transport system l during period t,

t CJIjipl

Unit transportation cost of product p from distribution center j to plant i in period t with transport system l,

Unit of production cost of product p at plant i in period t, Unit cost of each inspection and recycling of product p at the distribution center j in period t,

Capacity of supplier s for material r during period t, Production capacity at plant i in period t, Capacity of the distribution center j in period t, Unit transportation cost of raw material r from supplier s to plant i in period t, Unit transportation cost of product p from plant i to distribution center j in period t with transport system l,

t CCDcdp Unit transportation cost of product p from customer c to hybrid center d in period t, t CDIdip

Unit transportation cost of product p from hybrid center d to plant i in period t,

CTRtl t CUScp

Fixed cost of transporting material l during period t, Customer dissatisfaction cost for variable rate of receiving product p to customer c during period t, (continued)

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(continued) t ESIsir

Unit CO2 pollution emission resulting from the transfer of raw material r from the supplier s to the plant i during period t,

t EIJijpl

Unit CO2 pollution emission resulting from the transfer of product p from plant i to distribution center j in period t with transport system l,

t EJIjipl

Unit CO2 pollution emission resulting from the transfer of product p from distribution center j to plant i during period t with transport system l,

t EJCjcpl

Unit CO2 pollution emission resulting from the transfer of product p from distribution center j to customer c with transport system l during period t,

t ECip

Unit CO2 pollution emission resulting from the production of product p in plant i in period t,

JClt

Number of job creation by selecting transportation l during the period t,

nrp

Coefficient of consumption of raw material r in product p,

mp

Capacity occupation for producing product p,

Rp

Return rate of product p from distribution centers to plants,

Rpp

Percentage of processing product p from the returned products,

BM

An arbitrary big number

Decision variables t QSIsir

Amount of raw material r sent from the supplier s to plant i in period t,

QIipt

Amount of product p produced in plant i in period t,

t QIJijpl

Amount of the product p sent from plant i to the distribution center j with the transportation system l in period t,

IN Vjpt

Inventory of product p in distribution center j at the end of period t,

t QJCjcpl

Amount of product p shipped from distribution center j to the customer c with the transportation system l during the period t,

t QJIjipl

Amount of the product p sent from the distribution center j to the plant i with the transport system l during the period t,

t QCDcdp

Amount of product p shipped from the customer c to the hybrid center d during the period t,

t QDIdip

Amount of product p transferred from hybrid center d to plant i during the period t,

Yjt

A binary variable is equal to 1 if at the point j in period t, the distribution center is established,

Atijl

A binary variable is equal to 1 if the transportation system l connects the plant i and the distribution center j in period t,

t Bjcl

A binary variable is equal to 1 if the transportation system l connects the distribution center j to client c in period t.

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Mathematical model equations minimize Z1 =

  t

FYit (Yjt )

j

   t (QSIsir + QS0irt ) SCsrt + QIipt MCipt + t

+

t

+

+

t t QSIsir CSIsir

r

i

p

j

c

i

p

c

d

t t QJCjcpl CJCjcpl

l

p

j

t t QIJijpl CIJijpl

l

t t QJIjipl CJIjipl

l t t QCDcdp CCDcdp

p

d

  t

+

j

t

p

  t

+

i

p

l

  t

+

s

i t t QJCjcpl ICJp

IN Vjpt HCpt

  t

+

j

p

j

  t

+

c

  t

+

r

i

  t

+

s

 

t t QDIdip CDIdip

p

i

  t

j

i

l

t

j

c

p

Atijl CTRtl +

 

minimize Z2 =

t

p

(1)

t

s

i

t t QSIsir ESIsir

r

  t

i

j

p

j

c

p

i

j

p

t t QJCjcpl EJCjcpl

l

  t

t t QIJijpl EIJijpl

l

  t

+

l

QIipt ECipt

  t

+

c

l

i

+

j

t Bjcl CTRtl

  t−1 t t QJCjcpl CUScpl − QJCjcpl



+

 

l

t t QJIjipl EJIjipl

(2)

A Perishable Product Sustainable Supply Chain Network …



maximizeZ3 =

t

j

i

Atijl JClt +

109

 t

l

c

j

t Bjcl JClt

(3)

l

subject to  j

P

t nrp QIJijpl =



t QSIsir +

s

l

+

i

 P

d



IN Vjpt−1 +

l

l

∀i, r, t,





c

(4)

t QJCjcpl

l

t QJIjipl ∀j, p, t,

(5)

l

t t QJCjcpl = Dcp ∀c, p, t,

(6)

t  ≤t−LTcp

l



t t t QCDcdp = Dcp RCcp ∀c, p, t,

d



P

t nrp QJIjipl

t nrp QDIdipl

l

i

  j

j

t QIJijpl = IN Vjpt +

+

i



 =

t QDIdip



t

(7)

 t QCDcdp

RHpt ∀d , p, t,

(8)

c



t QSIsir ≤ CSsrt ∀s, r, t,

(9)

i

 j



p

IN Vjpt +

p

t mp QIJijpl ≤ CXit ∀i, t,

(10)

l

 c

p



t QJCjcpl ≤ CYjt Yjt ∀j, t,

(11)

l

Atijl ≤ 1 ∀i, j, t,

(12)

t Bjcl ≤ 1 ∀j, c, t,

(13)

l

 l

Atijl ≤



t QIJijpl ∀i, j, l, t,

(14)

t QJCjcpl ∀j, c, l, t,

(15)

p t ≤ Bjcl

 p

 p

t t QIJijpl + QJIjipl ≤ BMAtijl ∀i, j, l, t,

(16)

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t t QJCjcp ≤ BMBjcl ∀j, c, l, t,

(17)

t QJIjipl = Rp IN Vjpt ∀j, p, l, t,

(18)

p

 i



t QIJijpl

⎛ ⎞  t ⎠ = QIipt + Rpp ⎝ QJIjipl ∀i, p, l, t,

j

(19)

j

t t t t t t , QIipt , QIJijpl , IN Vjpt , QJCjcpl , QJIjipl , QCDcdp , QDIdip QSIsir t ≥ 0; Yjt , Atijl , Bjcl ∈ {0, 1}

∀i, j, p, l, t, s, c.

(20)

Equation (1) presents the total costs of each chain period. These costs include the fixed costs of establishing distribution centers and purchasing from suppliers, production costs, operating costs at DCs, inventory costs at distribution centers, transportation costs by various transportation systems in the supply chain, and customer dissatisfaction cost. Equation (2) also minimizes the total amount of CO2 released from product production and transportation along with different levels of the supply chain. Equation (3) maximizes the social responsibility of the supply chain and it consists of total created jobs in the supply chain. Equation (4) shows that the amount of raw material imported to each plant in each period is equal to the output of that plant in the same period. Equation (5) ensures that for each of the products in each of the periods, the amount entered into each distribution center as well as the remaining inventory of the previous period is equal to the amount sent to customers and the rest of the inventory at the end of the period. This equation is known as the balance of inventory. Equation (6) states that for each product and in each period, the available amount in each distribution center must be able to meet the demand for that product. This demand should be delivered in the specified lead time for each product and each customer. Equation (7) calculates the total received products in hybrid centers. Equation (8) calculates the number of reusable products in hybrid centers which are shipped to plants. Equation (9) ensures that the amount of each raw material sent from suppliers does not exceed their capacity. Equation (10) states the constraint of the capacity of materials in the factories as suppliers. Equation (11) states that the remaining inventory at each distribution center should not exceed their capacity. Equations (12) and (13) indicate that among all transportation systems, just one of them can be selected for transformation from a specified orientation and specified destination. Equations (14) and (15) point out that the transportation system is used when a shipment is transformed from orientation to a destination. Equations (16) and (17) state that members of a chain that does not have a connection no goods are also sent. Equation (18) determines the amount of returned goods as a percentage of the distributor’s inventory. Equation (19) points out that the amount of goods sent from

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the plant to the distribution centers is equal to the amount produced at the plant and is a percentage of returned products that are being redeveloped. The domain of the variables is given in Eq. (20)

4 Solution Method (HWOAGA Algorithm) Using novel meta-heuristic algorithm in optimizing NP-HARD problems are addressed and suggested in different researches in the field of optimization (Tirkolaee et al. 2019, 2020; Goli and Davoodi 2018; Davoodi and Goli 2019; Sangaiah et al. 2020). Conside ring that the designed mathematical model is a multi-objective model, multi-objective optimization methods should be used to solve this model. In this regard, HWOAGA and epsilon constraint are used. In the whales’ lifestyle, it is preferred to hunt small fishes near the water surface by creating bubbles surrounding the prey. The WOA algorithm is one of the nature-based and population-based optimization algorithms based on the Whale lifestyle that can be used in a variety of contexts. This algorithm is consists of sieging, bubble attack, and hunting. These methods have been designed into a mathematical formulation. In WOA, in the sieging operator, different solutions are achieved by increasing the value of a controllable parameter as a. By selecting random values for vector A between −1 and +1, a search agent can be applied. In bubble attach, first it calculates the distance between the wall located in the X* and Y. Then, the position of the whale will be updated to converge walls in the best possible location. Hunting operator is applied by using |A| > 1 mode, while the best solution is chosen when |A| < 1 to update the position of the search agents. The WOA algorithm has the ability to choose between circular or spiral motion. Finally, the WOA algorithm ends with satisfying the termination conditions. The whales can identify and surround the hunting grounds. Since the optimal design location in the search space is not known, by comparison, the algorithm assumes that the best candidate for the present is either target hunting or near-optimal. After the best search agent has been identified, other search agents try to update their location to the best search agent (Mirjalili and Lewis 2016). This behavior is expressed through Eqs. (21) and (22):   D = c.X ∗ (t) − X (t)

(21)

X (t + 1) = X ∗ (t) − A.D

(22)

In the above equations, t represents the iteration of the algorithm, C and A are the coefficients, and X* is the best position obtained and X(t) is the current wall position. It should be noted that the value of X* is updated in each iteration. Equations (23)-(24) are employed to determine the values of A and C.

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A = 2a.r − a

(23)

C = 2.r

(24)

As indicated before, a is a controllable parameter that is reduced iteration by iteration to reach zero at the final iteration (Mirjalili and Lewis 2016). Moreover, r is a random vector that takes value in [0, 1]. In order to design the HWOAGA, Eqs. (21)–(24) is utilized to apply sieging, bubble attack operators. Moreover, to have a better search in solution space, crossover and mutation operators are being used. However, as the proposed mathematical model is multi-objective, to rank the solutions and report best Pareto solutions, fast nondominate sorting which has been proposed by Deb (2000) is applied. The flowchart of the proposed hybrid algorithm is represented in Fig. 2. The defined chromosomes in this study follow two structures. In the first structure, binary variables are defined and vectors with values between 0 and 1 are generated. The cells with less than 0.5 value mean not-select/not-establish and if more than 0.5 means select/establish. In the second structure, positive variables are determined. In this structure, each cell will have a value between 0 and 1. This number specifies the percentage of sending materials or final products from a source to a destination in the supply chain. In order to meet the constraints on the flow of materials and products, the values in this structure will be normalized. For example, if the number of potential DCs is 3 and the number of customers is 5, an example of a chromosome designed according to Figs. 3 and 4 is shown. According to Fig. 3, DC2 and DC3 are established among the three distribution centers. In this regard, only two of these distributors will deliver products to customers. Therefore, the values of each column in structure 2 between DC2 and DC3 are normalized. The interpretation of this chromosome is presented in Fig. 4. According to Fig. 4, 52% of customer 1 demand is met by DC2 and the rest by DC3. According to the proposed solution representation, all constraints related to the material flow in the supply chain will be met. However, supply chain capacity constraints may be violated. To address this, a penalty will be added to all three objective functions if the facility capacity is violated.

5 Numerical Results In order to evaluate the performance of HWOAGA, first, the algorithm parameters are tuned by applying the Taguchi design of experiment method. Accordingly, three values (three levels) are suggested for each parameter and then, the best value for each parameter is obtained according to signal to noise index. The suggested and best value for HWOAGA parameters are presented in Table 2. It should be noted that the population size and maximum iterations are considered as a two related

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Fig. 2 Flowchart of the proposed HWOAGA algorithm Structure 1

0.41

0.72

Customer 1 Customer 2 Structure 2

0.93

Customer 3

Customer 4

Customer 5

DC1

0.61

0.29

0.43

0.27

0.35

DC2

0.45

0.73

0.28

0.34

0.19

DC3

0.35

0.91

0.73

0.58

0.39

Fig. 3 An example of proposed solution representation

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Structure 2

0

1

1

Customer 1

Customer 2

Customer 3

Customer 4

DC1

0

0

0

0

Customer 5 0

DC2

0.52

0.44

0.27

0.37

0.32

DC3

0.48

0.56

0.73

0.63

0.68

Fig. 4 Chromosome interpretation

Table 2 Taguchi results for HWOAGA Parameters

Levels

Best value

1

2

3

WOA controllable parameter (a)

2

1.5

1

2

Crossover percentage (Pc)

0.7

0.8

0.9

0.8

Mutation percentage (Pm)

0.3

0.2

0.1

0.1

(Population size, Number of iteration)

(200, 50)

(100, 100)

(50, 200)

(200, 50)

parameters and the common suggested value are applied. The main reason is that these parameters affect the solution time directly. After setting HWOAGA parameters, the performance of this algorithm is assessed. For this purpose, 10 test problems are generated in different dimensions using (Goli et al. 2019). The used indices for comparing the solution method are as Eqs. (25)–(30). SNS =

n i=1 (MID

− Ci )2 n−1

(25)

 Ci = f21 + f22 + f23

(26)

  3  Maxspread =  (Minfi − Maxfi )2

(27)

i=1

n MID =

RAS =

i=1



f 1i −f 1best min f 1max total −f 1total

2

+



f 2i −f 2best max min f 2total −f 2total

2

+



f 3i −f 3best min f 3max total −f 3total

n

n  f1i (x)−f1ibest (x)   f2i (x)−f2ibest (x)   f3i (x)−f3ibest (x)   +  f best (x)  +  f best (x)  i=1  f best (x) 1i

2i

n 

n   ¯ i=1 d − di SM = (n − 1)d¯

3i

2 (28)

(29)

(30)

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The information on these test problems is described in Table 3. These test problems are applied with HWOAGA and also Augmented Epsilon constraint (AEC) which is proposed by Sangaiah et al. (2019). It should be noted that for each method of solving time limitation is considered as 3600 s. After implementing HWOAGA and AEC with different test problems, some multi-objective indices are calculated and reported in Tables 4 and 5. Table 3 Information on numerical examples Test problems

# Suppliers

# Plants

# DC

# Hybrid centers

# Products

# Raw materials

# Transportation systems

# Time periods

1

5

2

4

2

1

1

1

7

2

7

3

6

3

3

2

1

7

3

9

4

8

4

5

3

1

9

4

11

5

10

5

7

4

2

9

5

13

6

12

6

9

5

2

12

6

15

7

14

7

11

6

2

12

7

17

8

16

8

13

7

3

15

8

19

9

18

9

15

8

3

15

9

21

10

20

10

17

9

3

20

10

23

12

22

12

19

10

4

20

Table 4 AEC results for solved test problems Test problems

MID

Max_Spread

SM

1

13.290

11.600

7.91

7

0.270

6.940

6.18

2

14.198

12.581

9.541326

10

0.353

9.558

12.95

3

15.261

14.930

10.06833

11

0.542

12.316

79.36

4

16.697

15.812

15.92292

11

0.723

17.144

425.85

5

17.244

16.501

19.61764

13

0.922

22.102

994.13

6

18.638

22.395

23.40268

15

1.547

22.914

1272.68

7

20.090

31.184

29.90024

17

2.112

33.732

2463.88

8

20.744

40.822

35.35586

19

3.072

35.858

3600.00

9

21.413

42.707

53.01155

19

3.660

48.813

3600.00

10 Average

0.000

0.000

17.508

23.170

NPS

RAS

Spacing

CPU time

0

0

0.000

0.000

Not solved

22.748

13.55

1.46

23.264

1383.892

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Table 5 HWOAGA results for solved test problems Test problem

MID

Max_Spread

NPS

RAS

Spacing

1

18.869

10.008

SNS 7.6622

6

0.345

7.065

CPU time 13.95

2

18.750

18.431

10.0910

18

0.463

10.935

15.97

3

15.845

16.542

11.1755

26

0.652

14.679

32.97

4

18.627

22.470

22.0571

29

0.957

18.436

57.49

5

25.336

20.822

24.8277

30

1.040

27.163

73.85

6

19.234

30.336

23.6967

30

1.577

32.113

96.28

7

29.121

39.136

38.2629

37

2.427

45.893

127.49

8

30.477

49.408

52.1947

43

4.393

43.912

133.64

9

23.715

47.347

65.1100

50

4.110

53.015

157.96

10

23.690

56.216

60.4938

50

6.326

66.930

169.93

Average

22.366

31.071

31.5572

31.9

2.229

32.014

87.953

5.1 Review the Solution Methods Based on the MID Index The average of this index for AEC was 17.5 and for the HWOAGA algorithm, it was 22.36. The results of this indicator indicate that HWOAGA amount was higher in all examples. The reason for this is that the AEC method introduces the best possible values, so for the innovative method, the small distance with these values is equivalent to the quality of this method. The results based on MID index are presented in Fig. 5. 35.000 30.000

MID

25.000 20.000 15.000 10.000 5.000 0.000 1

2

3

4

5

6

7

Test problem AEC

Fig. 5 Comparing HWOAGA and AEC on MID index

HWOAGA

8

9

10

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60.000

Max spread

50.000 40.000 30.000 20.000 10.000 0.000 1

2

3

4

5

6

7

8

9

10

Test problem AEC

HWOAGA

Fig. 6 Comparing HWOAGA and AEC on max spread index

5.2 Review the Solution Methods Based on the Max Spread Index The average of this index for the AEC was 23.1 and for the HWOAGA algorithm was 31.07. As shown in Fig. 6, the AEC method has only been able to provide a better value than the HWOAGA in the first test problem. In other ones, HWOAGA method provides a better value than this index, which indicates the effectiveness of this method in finding the solutions to the intended problem.

5.3 Review the Solution Methods Based on the SNS Index The average of this index for AEC was 22.7 and for the HWOAGA algorithm was 31.55. Regarding the SNS index, like the Max spread, the HWOAGA algorithm is also well-known for finding optimal solutions. By increasing the issue number, the superiority of the HWOAGA method is seen in the SNS index. The results based on SNS index are presented in Fig. 7.

5.4 Review the Solution Methods Based on the NPS Index The average of this index for the Epsilon constraint was 13.55 and for the HWOAGA algorithm was 31.90. The NPS index is one of the most important indicators that shows the superiority of the HWOAGA algorithm over AEC. In AEC method the ability to find various answers decreases due to constraints in the problem. But in the HWOAGA algorithm, due to its random and intelligent search, it is possible to find

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SNS

50 40 30 20 10 0 1

2

3

4

5

6

7

8

9

10

8

9

10

Tet problem HWOAGA

AEC

Fig. 7 Comparing HWOAGA and AEC on SNS index 60 50

NPS

40 30 20 10 0 1

2

3

4

5

6

7

Test problem AEC

HWOAGA

Fig. 8 Comparing HWOAGA and AEC on NPS index

more optimal solutions, which is why in the NPS index a much better performance of this algorithm is seen. The results based on NPS index are presented in Fig. 8.

5.5 Review the Solution Methods Based on the RAS Index The average of this index for the AEC was 1.46, and for the HWOAGA algorithm was 2.22. As it can be seen in Fig. 9, the HWOAGA algorithm offers more values in compare with AEC. This difference is small in a number of examples. The small difference between the HWOAGA algorithm and the AEC method, which is on average about 0.6, indicates the ability of the HWOAGA algorithm.

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7.000 6.000

RAS

5.000 4.000 3.000 2.000 1.000 0.000 1

2

3

4

5

6

7

8

9

10

Test problem HWOAGA

AEC

Fig. 9 Comparing HWOAGA and AEC on RAS index

5.6 Review the Solution Methods Based on the Spacing Index The average of this index for the AEC was 23.26 and for the HWOAGA algorithm was 32.01. The analysis of this index is similar to the RAS index. The AEC method provides fewer values, and the HWOAGA algorithm tries to provide a SNS close to this method. The above results show that this algorithm has been relatively successful in its attempt to be close to the ideal solution. The results based on Spacing index are presented in Fig. 10.

80.000 70.000

Spacing

60.000 50.000 40.000 30.000 20.000 10.000 0.000 1

2

3

4

5

6

7

Test problem AEC

HWOAGA

Fig. 10 Comparing HWOAGA and AEC on spacing index

8

9

10

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Fig. 11 Comparison of solving time of two methods

5.7 Review the Solution Methods Based on CPU Time The results show that the EAC has an average time of 1383.89 s. While this index for HWOAGA is 87.95. Figure 11 shows the comparison of the solving time of these two methods. As seen in Fig. 11, in the AEC method, the solving time has an ascending trend with a significant slope. It has solved test problem 7 and 8 as long as possible and also did not have capabilities to solve problem 10. This is while the HWOAGA algorithm has increased the solving time with a very small slope. In summary, after analyzing different indices, it is indicated that the HWOAGA algorithm, with spending much less time than AEC, can provide very close outputs to optimal answers and even works better on some other indicators than AEC. This issue properly shows the ability of the HWOAGA algorithm to solve the problem raised in this research.

5.8 Sensitivity Analysis In this section, the effect of the demand parameter on each objective function is investigated. The reason for choosing the demand parameter among all the parameters is that in real conditions the fluctuation of this parameter is much higher than other ones and on the other hand, it has a great impact on the supply chain structure. Accordingly, the fluctuation of −20 to +20% is considered on this parameter and the value of each objective function is measured. The results are summarized in Table 6. The trend of each objective by changing the demand parameter is illustrated as Figs. 12, 13 and 14. Figure 12 shows that with an increase in demand, a linear effect on the economic objective is observed. In other words, increasing demand causes supply chain costs

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Table 6 Sensitivity analysis results Fluctuation rate

−20%

−10%

0%

Z1

0

0

0

10% 0.019

20% 0.021

Z2

2230.45

2251.29

2307.12

2360.96

2399.81

Z3

862.95

872.98

883.01

893.04

903.07

Fig. 12 Effect of demand fluctuation on the first objective (economic)

Fig. 13 Effect of demand fluctuation on the second objective (environmental)

to be increased linearly. The results of Fig. 13 show that increasing demand leads to finding a growth in the second objective function with a non-constant slope. At the level of −20%, the lowest slope, and at the level of +20%, the highest value of this objective function is observed. Figure 14 shows that negative demand changes do not affect the social objective function. In other words, reducing the amount of demand

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Fig. 14 Effect of demand fluctuation on the third objective (social)

cannot change the social responsibility of the supply chain. However, increasing demand increases non-linear supply chain social responsibility.

6 Conclusion and Future Studies In this study, the optimization of multi-product and multi-period closed-loop supply chain design problem was investigated. The supply chain understudy was commensurate with the supply of perishable products. In this regard, the sustainable supply chain is considered as an essential and effective concept. The proposed mathematical model can provide suitable solutions because of considering all internal and external stations in the way of meeting customers’ demands. By considering the nature of dairy products, delivery time, and customer satisfaction in supply chain design were investigated. In order to optimize this mathematical model, a new meta-heuristic algorithm called HWOAGA was developed. In this algorithm, the algorithms of the whale optimization algorithm and Genetic algorithm were used. The results of comparing this algorithm with AEC show that the proposed algorithm produces much more Pareto solutions, the quality of Pareto solutions is acceptable and the dispersion is good. It also has a shorter time to solve test problems in comparison to the AEC method. The managerial insights of this study suggest that applying the mathematical model and the proposed solution method can help supply chain managers to improve financial, environmental, and social performance. In order to develop this research, it is suggested to develop the mathematical model under uncertainties in demand and environmental parameters by using possibilistic or stochastic programming. Also, the robust optimization approach can be an effective way of dealing with uncertainty in this problem.

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References Babazadeh R, Razmi J, Pishvaee MS, Rabbani M (2017) A sustainable second-generation biodiesel supply chain network design problem under risk. Omega 66:258–277 Burgess TF, Grimshaw P, Huaccho Huatuco L, Shaw NE (2017) Mapping the operations and supply chain management field: a journal governance perspective. Int J Oper Prod Manag 37(7):898–926 Cao C, Li C, Yang Q, Liu Y, Qu T (2018) A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters. J Clean Prod 174:1422–1435 Carter CR, Rogers DS (2008) Sustainable supply chain management: toward new theory in logistics management. Int J Phys Distrib Logist Manag 38(5):360–387 Davoodi SMR, Goli A (2019) An integrated disaster relief model based on covering tour using hybrid Benders decomposition and variable neighborhood search: application in the Iranian context. Comput Ind Eng 130:370–380 Deb K, Agrawal S, Pratap A, Meyarivan T (2000, September) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International conference on parallel problem solving from nature. Springer, Berlin, pp 849–858 Fard AMF, Hajaghaei-Keshteli M (2018) A tri-level location-allocation model for forward/reverse supply chain. Appl Soft Comput 62:328–346 Ghelichi Z, Saidi-Mehrabad M, Pishvaee MS (2018) A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: a case study. Energy 156:661–687 Goli A, Davoodi SMR (2018) Coordination policy for production and delivery scheduling in the closed loop supply chain. Prod Eng Res Devel 12(5):621–631 Goli A, Zare HK, Tavakkoli-Moghaddam R, Sadegheih A (2019) Multiobjective fuzzy mathematical model for a financially constrained closed-loop supply chain with labor employment. Comput Intel Liang L, Quesada HJ (2018) Green design of a cellulosic butanol supply chain network: a case study of sorghum stem bio-butanol in Missouri. BioResources 13(3):5617–5642 Mardan E, Govindan K, Mina H, Gholami-Zanjani SM (2019) An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem. J Clean Prod 235:1499–1514 Mardani A, Kannan D, Hooker RE, Ozkul S, Alrasheedi M, Tirkolaee EB (2020) Evaluation of green and sustainable supply chain management using structural equation modelling: a systematic review of the state of the art literature and recommendations for future research. J Clean Prod 249:119383 Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67 Niranjan T, Parthiban P, Sundaram K, Jeyaganesan PN (2019) Designing a omnichannel closed loop green supply chain network adapting preferences of rational customers. S¯adhan¯a 44(3):60 Rabbani M, Hosseini-Mokhallesun SAA, Ordibazar AH, Farrokhi-Asl H (2020) A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design. Int J Syst Sci Oper Logist 7(1):60–75 Rad RS, Nahavandi N (2018) A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. J Clean Prod Sangaiah AK, Tirkolaee EB, Goli A, Dehnavi-Arani S (2019) Robust optimization and mixedinteger linear programming model for LNG supply chain planning problem. Soft Comput 1–21 Sangaiah AK, Goli A, Tirkolaee EB, Ranjbar-Bourani M, Pandey HM, Zhang, W (2020) Big datadriven cognitive computing system for optimization of social media analytics. IEEE Access 8:82215-82226 Tirkolaee EB, Goli A, Weber GW (2019) Multi-objective aggregate production planning model considering overtime and outsourcing options under fuzzy seasonal demand. In: Advances in manufacturing II. Springer, Cham, pp 81–96

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Tirkolaee EB, Goli A, Weber GW (2020) Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.2998174 Yavari M, Geraeli M (2019) Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. J Clean Prod 226:282–305 Zhuo H, Wei S (2017) Gaming of green supply chain members under government subsidies—based on the perspective of demand uncertainty. In: Proceedings of the tenth international conference on management science and engineering management. Springer, Singapore, pp 1105–1116

Practical Aspects of Application the 3R (Reduce, Recycle, Reuse)

How to Assess Internal Transport in Terms of Sustainability in the Recycling Industry?—Case Study Izabela Kudelska and Monika Kosacka-Olejnik

Abstract Auxiliary processes are relevant element of the production process, due to the fact that they affect the technical and economic efficiency. In the modern production process there are many material flows between which or during which technological operations that process the input material into a finished product are carried out. Incorrect design or inadequate organization of supporting processes can cause serious disruptions or even financial losses, what may be analyzed from the perspective of sustainability. The chapter is focused on the process of internal transport, what is justified by influence of that process on the company’s economic effects, the quality of employees’ work and the quality of the natural environment. Authors analyze the internal transport process in a company representing recycling industry which is Endof-Life Vehicles disassembling enterprise. This sector is affecting People and Planet during generating Profits (3P concept). In disassembling company internal transport process includes transport of: ELV’s, vehicles parts, waste, but also it covers handling of materials including laying, fixing and measuring. Material flow is complex because of diversity in terms of materials’ size, shape, mass and quantity. In the result the internal transport process requires precise organization, which should be adopted to the company to ensure safe shipment of cargo with minimal costs. The basic purpose of the chapter is to prepare a proposal of method for internal transport assessment in terms of sustainability. Authors use the following research methods: literature review, case study, brainstorming. Chapter consists of three parts. The first part shows the role of the internal transport process as well as the differences between the transport process in a typical enterprise and disassembling company. In the second part, material flows occurring in disassembling facility are described. In the last part problems were identified and the directions of further research were indicated. Keywords Internal transport · Disassembling company · Sustainable development

I. Kudelska (B) · M. Kosacka-Olejnik Faculty of Engineering Management, Pozna´n University of Technology, Pozna´n 60-965, Poland e-mail: [email protected] © Springer-Verlag GmbH Germany, part of Springer Nature 2020 P. Golinska-Dawson (ed.), Logistics Operations and Management for Recycling and Reuse, EcoProduction, https://doi.org/10.1007/978-3-642-33857-1_7

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1 Introduction In authors’ opinion there is not attached enough attention to the issue of internal transport process both in Business and Academia. Internal transport is considered in the paper as an essential part of inbound logistics, affecting the most a production process, and as a consequence logistics activities supporting it. In the paper the internal transport process includes all activities related to material handling in order to: supply the production process, remove waste and prepare goods for distribution. All undertaken activities have to occur at the company’s area. Authors of the paper treat internal transport as a process that mainly supports production. Considering that, it was stated that internal transport process plays an important role in a production process owing to the fact that it shortens the production cycle, gives rhythm, and facilitates work. As a consequence, it was claimed that the transport process should ensure the movement of a certain amount of cargo on the shortest possible transport routes, with the maximum use of means of transport and the minimum use of the machinery. Following previous studies (Nowoty´nska et al. 2017a, b; Dima 2013), it was stated that transport includes a set of activities such as: moving, reloading and manipulations (stacking, fixing, counting). As a result of effective transport control, output parameters of the production process, including: the level of manufacturing costs, the volume of stocks and work-in-progress as well as work efficiency, are satisfactory for managers. Authors claimed that an economic efficiency of production depends not only on modern technology, but also on properly designed and organized process of internal transport, what justifies works on the specified topic. Considering the high importance level of internal transport process for the business activity, authors chose a disassembling company as an object of the conducted research. Disassembling facility was strongly recommended as company with high impact on people, planet and profit, on the basis of previous research conducted by authors of the paper (Kudelska and Kosacka 2014, Project of number 503223/11/140/DSMK/4132: Development of a method for the management of parts resulting from the dismantling of end-of-life vehicles taking into account the principles of sustainable development; Kosacka and Kudelska 2015, Project of number 503225/11/140/DSMK/4136: Development of a method for storing parts resulting from the dismantling of end-of-life vehicles). What is more it has been observed an increasing trend in vehicles number, what results in high growth of vehicle recycling sector in Poland. Considering the fact, that companies and customers are more aware of sustainability, it requires to introduce sustainability in business activity and all processes, including internal transport process. However there are made efforts on adopt sustainability issues in business activity, there is lack of solutions dedicated for internal transport. In authors opinion companies should have possibility to improve their impact on people, planet and profit considering their internal transport activity. Means of the internal transport often require manual operation. Authors claimed that, in many Polish companies a large part of the work is carried out by hand or by simple means of transport, what affects people. Even when there is a cutting-edge

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technology there is always required a man who control it. Moreover, all means of transport generate environmental burden and costs for the company. Taking that into consideration it was stated that internal transport should be sustainable. In order to meet the specified need, authors prepared a procedure for assessment of the internal transport realization to support sustainability policy introduction. It was stated that companies increase their competitiveness becoming more sustainable, but the problem lies in answering the question: how to check it? particularly in the context of internal transport. Consequently, there was prepared a list of research questions: • Q1: What requirements should meet an internal transport process in terms of sustainability? • Q2: What is the structure of the internal transport process realized in disassembling company? • Q3: How to assess the internal transport process in disassembling company in terms of sustainability? In order to achieve the specified goals there was used the case study method. The paper consists of four parts. In the Sect. 2, there was presented a disassembling company as a specific kind of business from the perspective of material flows and processes. Section 3 was focused on the theoretical background of the proposed method. On the basis of this part, the method was verified in the next parts. In the summary there were included directions for future research.

2 Disassembling Company as a Specific Business Domain In order to understand the specific features of disassembling company as representative of the recycling sector, in the presented chapter there were presented material flows and processes realized in that kind of business.

2.1 Material Flows Organization in a Disassembling Company Disassembling facilities operating in Poland have become a research object in the presented study. It was stated, that disassembling company becomes a key player in the recycling network, which is responsible for hazardous waste management (End-of-Life vehicles), affecting all sustainability pillars (Kosacka and Goli´nska 2014). It was stated, that the importance level of disassembling company, considering Environment and People, is relatively high. In the result it is under law supervision of the Recycling Act of ELVs [polish adaptation of Directive 2000/53/CE (2000)] in

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Fig. 1 Material flows in disassembling and manufacturing business—comparison

the aspect of: recycling and recovery rate, technical and organizational conditions, waste and parts storage, etc. In order to discover differences and similarities of disassembling company and typical manufacturer, there was made a material flow comparison, presented in the Fig. 1 With reference to Fig. 1, it was stated, that in manufacturing company material flows begin with the supply of raw materials from external transport to the main warehouse and then by internal transport to the departments. Departmental transport enables the flow of materials through production sections. It ends with inter— departmental transport directed to the finished goods warehouse, from which the distribution begins. In contrast, in disassembling facility there are supplied complex products—ELV’s. Moreover, there may be perceived also differences in output, as there are two main groups of materials, including: reusable parts (bought by endusers, car mechanics, shops with car parts) and waste intended for recycling or utilization by proper companies. The next differentiating factor is related to types of customers and suppliers, as key suppliers for disassembling companies are and-users of vehicles, also collection points, insurance companies and public authority. What is more, supplier becomes very often a customer, what is very specific situation on the market. In previous study authors have examined, that the most valuable flow is flow of reusable parts (Czwajda et al. 2017), that is the most diversified and requires appropriate warehousing as well as well-fitted infrastructure.

2.2 Processes in a Disassembling Company It was stated, that internal transport is always related to the primary production process, as it is a part of it, so firstly there should be recognized the disassembling process in order to analyze transport process.

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Fig. 2 Architecture of processes in disassembling facility

The disassembling process is controlled by demand on parts which is uncertain. Customer may order part directly in place, where company is located or via the Internet or phone. In the disassembling company, there are made following processes: ELV’s adoption,1 preparation for storage, vehicles storage, disassembling of vehicle or particular part (e.g. engine), waste/parts’ storage, waste/parts’ distribution. Processes are connected with material and information flows, however they would not occur without transport operations (internal and external), what was presented in the Fig. 2. With reference to Fig. 2, ELV’s adoption process is proceeded by external transport which may be realized by supplier or by receiver (disassembling facility). During adoption process ELV is weighted, what requires movement to the weight station. After completing the formalities, vehicles is moved to the station where preparation is made. There are removed some unique parts as well as waste (e.g. catalyst, fluids), which require transport. Prepared ELV is moved to the storage yard for vehicles, where it is kept until order for parts appears or disassembling request is made, in accordance to disassembling schedule. During storage, there are removed waste, which require transport. There are two options for disassembling. Firstly, fragmentary disassembling, where part is removed according to the order (sell at place). Part is transported to the sales department, where the payment is realized or it is stored until the shipment is made. Secondly, there is a full disassembling, if ordered part is hard to reach and it requires full disassembling or if vehicle is planned to be disassembled as a whole according to schedule. It was stated, that there is no procedure for disassembling process realization because scope of work is dependent on many

1 This

process was presented in details in previous study in work: (Kosacka et al. 2015).

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Fig. 3 Disassembled parts

factors including: the technical condition of parts, quality of parts’ connections, technical solutions made by car manufacturer. All disassembled parts are presented in the Fig. 3. With reference to Fig. 3, it was stated that the internal transport requires a lot of work in company under study. Disassembling process has the greatest demand on transport operations due to the fact that it results in generation of the large amount of waste and parts, which should be moved to appropriate storage container/area. Some of parts like engine, car seats are disassembled separately at appropriate stations, what also generates demand on transport. Considering the fact that, Polish recycling system is manual system, where there are simple machines and tools supporting manual operations made by people, it was stated that all processes in disassembling company are affecting people substantially. What is more, considering the specific kind of business it may be perceived a high influence of disassembling business on sustainable development policy realization.

3 Assessment of the Internal Transport Process in Terms of Sustainability—Methodology In order to answer the question: “How to assess the internal transport process in terms of sustainability”, there was developed a procedure presented in the Fig. 4: Firstly, there should be prepared a list of guidelines on internal transport consideration in the aspect of sustainability. However the term “Sustainable development” is widely known from 1987, the definition proposed by WCED (Brundtland 1987), is unclear and without any practical connotations. In the result it was claimed, that the term sustainability is not

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Fig. 4 Procedure for the internal transport assessment considering sustainability issues

understood by people. Authors claimed that it is not possible to assess the sustainability of each process in such of conditions, so there was made an attempt to define requirements for internal transport process that would be realised in terms of sustainable development policy introduction. In order to solve that problem, there was used a literature review method. The basis for sustainable development are three pillars, including social, ecological and economic aspects. As it was mentioned in the Introduction, internal transport process affects people, planet and economy. In the result it was stated that internal transport process which support sustainable development policy realization, should ensure transport of various materials (raw materials, semi-finished products, materials and the collection of finished products and waste) between stations

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and/or departments at the company, that would meet the following requirements simultaneously: • R1: cost reduction; • R2: comfort for the Employee improvement; • R3: negative influence on the Environment reduction through machines and organization that will ensure future generations access to the Environment at least at the same level that we have. With reference to the Fig. 4 according to the guidelines on internal transport consideration in the aspect of sustainability, there should be made an analysis of transport realization in details with the use of case study method. Authors recommend to perform some observations in the company under study. Firstly, there should be identified operations and parties engaged into their realization in order to map the process realization. Secondly, there should be identified resources, including human resources and material resources (means of transport and material). It is relevant to define the labour intensity of activities (influence on people) and the volume of work that should be done. Lastly, there should be made an analysis of factors affecting transport in the particular company. Authors recommended to use an Ishikawa diagram for that purpose. After the deep analysis of the internal transport in the company, there should be defined a list of achievements for the internal transport process in terms of sustainability, considering requirements listed beforehand (R1–R3). There was recommended the brainstorming method use with experts from the industry and academia. In authors opinion, in order to ensure simplicity of the assessment, there should be determined a questionnaire with a list of achievements that allow the assessment, according to the objective of the paper. Achievements should be divided into three groups, in order to assess separately each pillar of sustainability, what corresponds with the requirements R1–R3. Each achievement should be formed as a question with possible answer: YES, NO. In authors opinion each of achievement should be equally important. The level of accomplished achievements allow to evaluate the internal transport process in each of considered aspects. The obtained result will be in a range of 0– 100%. The biggest advantage is that all achievement that were not accomplished show direction for future development for the company. Authors recommended to use an obtained score to define future actions, not to point out what should be done but what is time pressure for changes, following the guidelines presented in the Table 1. Table 1 Guidelines for future actions Level

Score of assessment (%)

Required actions

Time pressure

1

Required

Immediately

2

(33–66>

Corrective

When organizational and financial state allow for that

3

(66–100>

Improvement

Expected but not required

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With reference to data shown in the Table 1, there were prepared guidelines which should be followed during assessment. Authors suggested to use simple scale with three ranges of percentage value of accomplished achievements, specified in the previous step. There were noticed information about time pressure of required actions, however there were not included guidelines what to do. Owing to the fact that, decisions should be made by Employees of the specified company, there is only obtained result of total score and the required minimum to achieve better level of assessment. The procedure described theoretically in presented chapter was verified in the next chapter for a disassembling company, which represents vehicle recycling sector in Poland.

4 Assessment of the Internal Transport Process in Terms of Sustainability in the Recycling Sector The method proposed in the previous section was used in order to assess the internal transport process in the one of the biggest polish disassembling facilities. The company employs 10 people, including the owner, processing around 1200 ELV’s annually. With reference to Fig. 4, following requirements R1–R3, there was made an analysis of internal transport realization in the company under study. Internal transport in disassembling company includes operations presented in the Fig. 2, where there were described operations, engaged parties and material stream. Considering that, authors stated that those operations may be divided into following types: • Production transport—transport operation related to the primary process (disassembling); • Warehouse-production transport—related to waste and parts reception and shipping, including their storage in warehouse area; • Inter-departmental transport, as a part of production transport, realized inside Disassembling Department, divided into working-stand type (at disassembling position) and transport between stations (e.g. between engine disassembling station and car seats disassembling station). Considering fact that Polish recycling companies have limited resources, which are engaged into many manual operations, there have been made analysis of resources, including means of transport used in a typical disassembling company, people engagement and material flows, what was described in the Table 2.

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Table 2 Means of transport used in disassembling facility Load unit

Transport route Start

End

Packaging unit

Means of transport Forklift

ELV

ELV’s’ storage Disassembling – yard station (car lift)

Engine

Disassembling Disassembling station (car lift) station

–

Cooler

Disassembling Disassembling station (car lift) station

–

Body

Disassembling Shredder station (car lift)

–

x

Wheel

Disassembling Vulcanization station (car lift) position

–

x

LPG installation/fuel tank

Disassembling Waste station (car lift) container

–

suspension system

Disassembling Parts’ station (car lift) warehouse

–

x

Liquids

Disassembling Waste station (car lift) containers

Barrels for liquids

x

Headlights, bumpers

Disassembling Parts’ station (car lift) warehouse

–

Car seat

Disassembling Seats’ station (car lift) container

–

Glass

Disassembling Parts’ station (car lift) warehouse

–

Dashboard

Disassembling Container for station (car lift) plastics

–

Other waste

Disassembling Containers station (car lift)

Platform trolley

Worker

x x x

x

x

x x

x x

x x

With reference to information in the Table 2, it was stated that however there are used forklifts and platform trolleys, vast of transport operations in disassembling facility is performed by people. Consequently, it was stated, that internal transport in disassembling company is very labour-intensive, as workers are very often responsible for transport. In the infrastructure of internal transport besides: car lift, forklift, supermarket trolley, platform trolley, there should be also distinguished transport routes, including transport routes inside disassembling hall and warehouse, and outside transport routes, between particular departments (ELVs’ storage yard, vulcanization station, etc.).

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Transport is made between stations and departments, what is a result of transport relations, responsible for the movement of the material. The following groups of relationships were distinguished: • transport relations; • delivery relations; • reception relations. With reference to Table 1, it was stated that transport analysis is much easier and its result is more transparent if it can be presented in a simple and compact way. It was recommended to examine the volume of material flows with the use of Sankey diagram as a specific type of flow diagram, in which the width of the arrows is shown proportionally to the flow quantity (Fig. 5). In the Fig. 5, there was presented Sankey diagram which represents all material flows into company under study, including ELVs (blue), waste (green) and parts (orange). The widths of the bands are directly proportional to the volume of items. The scheme puts a visual emphasis on the major transfers or flows within a system. It was claimed, that material flows are complex. The material volume that flows between individual objects is hard to identify because ELVs represent various completeness levels. In addition, parts are in a different technical condition, which may cause that they may not be redistributable. Authors stated that parts create the most diversified stream, which includes: gearbox, alternator, clutch, axle, wheel hub, starter, drive shaft, flywheel, bridge, engine, radiator, shock absorber, spring, rocker, beam, McPherson column, lamps, door, bonnet, rear hatch, bumper, fender, mirror, windows, seat, rear seat, navigation, gas installation, roof rack, trailer hook, tire, rim, hubcap, unique elements. It was stressed, that an essential part of transport process description is its correct identification, what is equal to decomposition at various hierarchical levels: system, environment, process and related attributes. Decomposition was made on processes occurring in disassembling company. In each of these processes, the relationships that occur between the various stages of disassembling process, have been isolated and analysed. In the result, there were identified factors influencing the increase in labour intensity of the means of transport. What is more, research and observation in a chosen disassembling facility, allowed to define and systematize the factors occurring and affecting the growth of lead time of transport operations, what (Table 3). Factors presented in the Table 3 show that, with the increase of labour consumption there is related growth of transport costs resulting even from poor surfaces, insufficiently wide passageways or insufficient service. Considering that, authors stated that identification of those factors and determination their impact, is essential for disassembling facility assessed in the context of sustainability issues. Considering the disassembling process in cybernetic terms, it is a series of consecutive operations from which the output of the previous operation is part of the next operation’s input, what was shown in Fig. 6. With reference to Fig. 6, it was noticed that there is a series of material displacements during which technological operations are carried out. Therefore, each process

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Fig. 5 Sankey diagram for material flows in disassembling company (adopted from Report of research on Sustainability in Remanufacturing operations, SIRO 2012–2014)

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Table 3 Factors affecting the increase of working time of the means of transport in disassembling facility (adopted from Fijałkowski 2003) Group of factors

Factor type

Description

Human factors

Experience

Possibilities and effort of a given operator in relation to the experienced operator (model)

Tiredness

Requirement of rest time

Inattention

Lack of proper supervision may result in longer transport time as well as increase the risk of transported part’s damage

Working parameters Distance

Lack of use of maximum speed at short distance

Height

Setting in layers or on higher shelves requires additional time

Driving direction

Varied time of forward and backward movement

Technical condition The technical condition of the means of transport has an impact on time losses (e.g. frequent failures) as well as on the environment Utilization factors

Pedestrian traffic

Delays caused by pedestrians and other vehicles

Obstacles

Low and narrow routes, obstacles at the routes

Surface

The type and condition of the surface on which the means of transport is moving

Doors/gates

At some of the doors/gates, the transport must be released because of its size

Cargo type and size The value of the movement times depends on the volume of the load, weight and type Lighting

The lighting quality affects transport as it requires more caution

Order

Disorder on the transport and storage surface delays the work of the means of transport

Weather

Poor weather (rain/snow) reduces work efficiency outside the disassembly hall

Elevation

Elevations limit the speed of the means of transport

Fig. 6 The cybernetic approach to the transport process in disassembling company

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Fig. 7 Factors affecting transport in disassembling company

should correspond to the proper transport process, because only then can you get the required economic and technical results. Consequently, it was stated that, poor transport organization in many cases cause serious disruptions of the disassembling process, including: • Unforeseen breaks, resulting from unpunctual delivery of ELV or waste/parts collection; • Growth of demand on storage area; • Changes in employees’ responsibilities e.g. disassembling workers deal with transport activities; • Deterioration of health and safety at work. It was claimed that many factors have an influence on transport in disassembling company, however they affect each other, e.g. lack of parts/waste removal from the dismantling station will require storage at the station, which will: reduce the storage space, make work more difficult for the employee considering aspect of health and safety and increase the risk of environmental pollution. There was made an analysis of all factors affecting transport, presented in the Fig. 7. According to Fig. 7, there were specified five groups of factors, including characteristic of input (ELVs), characteristic of output (parts, waste), employees, technology and market factors. On the basis of observations and interviews with employees of the company under study, as well as literature review, there were specified basic transport methods. The preliminary issue is the analysis of transport methods, which depends on the following factors: • • • • • • •

What to transport (material type, shape, weight, dimensions)? In what it should be transported (container)? Where it should be transported (route, start and end)? How often to transport? What are the transport distances? At what speed transport should take place? What are the transport routes (pavement hardness, slope)?

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• What should be the degree of mechanization or automation. To sum up, the above characteristic of internal transport process in the disassembling facility was used in order to assess it according to sustainability policy realization. With reference to the procedure presented in the Fig. 4, in the next step there was prepared a brainstorming session, where there were three researchers from area of production familiar with sustainability issues and two representatives of the disassembling facility (owner, production manager). One author of the paper was a chairman of brainstorming session, the other noted all ideas. In the result of conducted research, there were prepared three lists of achievements for each sustainability requirement determined for internal transport process in disassembling facility, presented in the Tables 4, 5 and 6. The first list of achievements was prepared for the economic issues, considering requirement R1, assessed for the company under study was presented in the Table 4. According to the prepared list of achievements (Table 4), the company where the research was carried out, obtained a result of economic aspect of development of internal transport at the level of 50%. In that case, it was claimed that company should improve their result, considering min. two changes in internal transport process to achieve the level of min. 66% (Table 1, level 2), which should be introduced when it is possible considering organizational and financial conditions. The second list of achievements was prepared for the social issues, considering requirement R2, assessed for the company under study. With regard to engagement of workers in transport operations in disassembling facility, authors recommended to analyse actions towards employees, considering the list of achievements (Table 5). Table 4 List of achievements to assess economic aspect of internal transport Achievement

YES NO

There many accidents in transport (min. one per day) There empty running during the transport

X X

There are often breaks in the dismantling process caused by the bad ELVs’ delivery X organization and the parts’ receipt/supply to the station (min. three per day) The depreciation costs of transport infrastructure are high

X

A lot of materials damaged during internal transport (min. 20% of total)

X

Each means of transport is empty for more than 20% of working time

X

Transport operations are not scheduled

X

There are many reloading points on the ELV’s disassembly route

X

There are many unnecessary operations of handing and transporting materials

X

There are some changes of means of transport in transporting each part/waste from start to end?

X

Total number of answers

5

Total score of economic aspect (%)

50

5

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Table 5 List of achievements to assess social aspect of internal transport Achievement

YES

NO

There are accidents occurring at work during internal transport (min. once per week)?

X

Employee responsible for internal transport have a wide range of duties also not related to transport

X

There is no employee’s satisfaction research in the company

X

There are many manual activities during transport

X

Manual transport may be replaced by mechanical transport

X

There is no close relationships with customers and suppliers

X

Most of means of transport pose a threat to the life and health of the employee

X

Most of means of transport are not ergonomic

X

Worker has to move manually a load with a weight exceeding 25 kg?

X

There is no customers’ satisfaction research in the company

X

Total number of answers

6

Total score of social aspect (%)

40

4

Table 6 List of achievements to assess ecological aspect of internal transport Question

YES

Cargo units with waste are not secured during transport and storage Containers/canisters are damaged

X

Waste/parts are temporarily stored on the floor

X

There are used even broken means of internal transport

X

Means of transport were not considered according to ecological parameters

X

Road transport surface is destroyed

X

Transport units are transported incorrectly Liquid and gaseous materials are not transported in separate containers

NO X

X X

Forklifts are not adapted to the working conditions

X

Forklifts have invalid current technical inspection documents

X

Total number of answers

5

Total score of ecological aspect (%)

50

5

Considering disassembling business there should be noticed, its internal and external stakeholders. Internal activity is related to employees, while external activity is directed at customers, suppliers and the local community. It was assumed, that activities in the social area can bring many benefits. These benefits can be considered in the external dimension: improving the image and reputation, increasing customers’ loyalty, as well as in the internal dimension: maintaining the best employees, increasing the motivation of managers and employees, improving relationships with employees, that is based on trust, integration and group cohesion.

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According to the prepared list of achievements (Table 5), the company where the research was carried out, obtained a result of social aspect of development of internal transport at the level of 40%, below the score of economic aspect. In that case, it was claimed that company should improve their result, considering min. three changes in internal transport process to achieve the level of min. 66% (Table 1, level 2), which should be introduced when it is possible considering organizational and financial conditions. The last list of achievements was prepared for the ecological issues, considering requirement R3, assessed for the company under study. As it was stated before, disassembling facility affects not the Environment due to the fact that it manages hazardous waste. A list of achievements for the particular aspect was presented in the Table 6. According to the prepared list of achievements (Table 6), the company where the research was carried out, obtained a result of ecological aspect of development of internal transport at the level of 50%, below the score of economic aspect. In that case, it was claimed that company should improve their result, considering min. two changes in internal transport process to achieve the level of min. 66% (Table 1, level 2), which should be introduced when it is possible considering organizational and financial conditions. To sum up, disassembling facility has limited human, financial and material resources. For the proper implementation of tasks from the perspective of implementing the principles of sustainable development policy, this business require a support for the decision-making process in the area of internal transport. However, internal transport is often treated superficially, it was stated that it is essential for all processes undertaken in the disassembling facility, what justifies research on that topic.

5 Summary Sustainability is a modern and relevant issue which should be practically implemented at the operational level of business, however the translation is not so easy and require experts knowledge. In the paper authors have chosen disassembling company considering high potential in the context of sustainability in order to prepare the assessment method for internal transport process evaluation. It was claimed, that internal transport is essential and it influenced other business activities. According to the research the main factors, that have an impact on transport and the choice of a means of transport are: the shape, size, construction of the good. When the transport is designed, there should be answered the following questions: • Why and what kind of material have to be transported? • Where is start and stop for material transport and why? • When and what is the amount of material to be transported?

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Considering those questions, there may be eliminated the randomness in the internal transport design solution. Moreover, during the analysis, it is not enough to examine only technical aspects, but also the economic, social and environmental criteria should be noted. In the paper there was determined a procedure that allow assessment of the internal transport in terms of sustainability, what is the biggest advantage in authors opinion. The presented method requires some improvements, however the most relevant is that the objective was to consider meaningful of the internal transport from the perspective of the sustainability issues. Authors are aware of the limitation of the proposed method as the list of achievement is opened and it was specified for particular company, however it should be considered as an advantage. The list may be improved and fit for a company. In future research authors plan to create more precise performance measurement system to assess not only each of sustainability aspects, but also sustainability state of the whole internal transport as well as other processes. Acknowledgements This paper refers to the research conducted under Statutory activity, financed by MNiSW/Pozna´n University of Technology, Project ID: 11/140/DSMK/4136.

References Brundtland GH (1987) Report of the World Commission on environment and development: our common future. United Nations Czwajda L, Kosacka M, Kudelska I (2017) Prediction markets as a decision support tool in disassembling companies. DEStech Transactions on Engineering and Technology Research, (ICPR2017), pp 238–243 Dima IC (2013) Industrial production management in flexible manufacturing systems. Published in the United States of America by Engineering Science Reference, IGI Global Directive 2000/53/EC (2000) Directive 2000/53/EC of the European Parliament and the Council on end-of-life vehicles (2000). Off J Eur Commun Fijałkowski J (2003) Transport wewn˛etrzny w systemach logistycznych. Warsaw University of Technology Publishing House, Warsaw Kosacka M, Goli´nska P (2014) Assesment of sustainability in dismantling station-case study. Res Logist Prod 4(2):135–145 Kosacka M, Kudelska I (2015) Project of number 503225/11/140/DSMK/4136: development of a method for storing parts resulting from the dismantling of end-of-life vehicles Kosacka M, Kudelska I, Goli´nska-Dawson P (2015) How properly value ELVs? Concept of the tool of ELVs assessment for dismantling station. Case study. In: Proceedings of the 25th international conference on flexible automation and intelligent manufacturing (FAIM 2015), pp 208–215 Kudelska I, Kosacka M (2014) Project of number 503223/11/140/DSMK/4132: development of a method for the management of parts resulting from the dismantling of end-of-life vehicles taking into account the principles of sustainable development Nowoty´nska I, St Kut, Krauz M (2017a) Internal transport as integrated logistics parts in production part 1. BUSES Technol Oper Transp Syst R 18(12):1548–1551 Nowoty´nska I, St Kut, Krauz M (2017b) Internal transport as integrated logistics parts in production part 2. BUSES Technol Oper Transp Syst R 18(12):1552–1555 Report of research on Sustainability in Remanufacturing operations. SIRO, 2012–2014

Principle of the Cognitive Grinding of Reuse Materials Adam Mrozinski, ´ Józef Flizikowski, Kazimierz Bielinski, ´ and Marek Macko

Abstract Cognitive grinding or active recycled and reuse machinery monitoring— continuous supervising, diagnosing, managing, controlling, compensating, documenting; a process of acquiring and transferring streams of information (usually source information) about the analyzed, developed object, process and relations between the same and the environment that can be used to realize the postulated state: knowledge creation (theory and innovation), environment melioration (harmfulness) and technical system optimization (design)—depending on technology needs and engineer imaginations. The objective of this paper is to provide a mathematical description, optimization of the states and changes in the grinding of recycled materials and machine space, their surface and volume during movement (idle and working movement) of the components and design assemblies in the multi-hole grinding process. Keywords Cognitive grinding · Recycling · Machinery monitoring

1 Introduction Knowledge creation, coming as result of the system development, optimization, modernization and innovation—creative action (creating). Melioration—intentional activities of a technical system and boundary zone; activities that enhance, improve A. Mrozi´nski (B) · J. Flizikowski · K. Bieli´nski Faculty of Mechanical Engineering, University of Technology and Life Science in Bydgoszcz, Kaliskiego 7 Street, 85-789 Bydgoszcz, Poland e-mail: [email protected] J. Flizikowski e-mail: [email protected] K. Bieli´nski e-mail: [email protected] M. Macko Kazimierz Wielki University in Bydgoszcz, Chodkiewicza 30 Street, 85-064 Bydgoszcz, Poland e-mail: [email protected] © Springer-Verlag GmbH Germany, part of Springer Nature 2020 P. Golinska-Dawson (ed.), Logistics Operations and Management for Recycling and Reuse, EcoProduction, https://doi.org/10.1007/978-3-642-33857-1_8

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and restore properties of the environment and not only limit technological harmfulness. Optimum—coming in property of the machinery design (construction) or system state with respect to the criteria that enable rational evaluation of the state. Active, cognitive grinding and monitoring, investigations into multi-disc grinders demonstrate that it is possible to acquire knowledge of, describe and utilize, for design and structural purposes, the characteristics that indicate the relations between speeds, idle movement, loads and the indicators of motion variables in the grinding space. The objective of this paper is to provide a mathematical description, optimization of the states and changes in the grinding recycled materials and machine space, their surface and volume during movement (idle and working movement) of the components and design assemblies in the multi-hole grinding process.

2 Process Engineering The development of the process, devices, system and machinery construction in the innovation of the plastics engineering depends on a motivation, knowledge, capital and ready markets. The innovation is carried on methodically, on the basis of the system mathematical model. The versatile equation including all the novelty beings in the operating systems, from idea till elimination, has the form:   ¯ R, ¯ Θ, t = P(¯s , z¯ , Θ, t − t0 ), L H¯ , E,

(1)

where: H¯ —performance characteristics as output quantities (efficiency), ¯ E—inner elements ((nS) construction) and outer elements (ready markets), ¯ R—connections of elements (relations, reactions, correlations of elements), Θ, (t1 − t0 )—time, s¯ —intentional control, z¯ —disturbances. According to designation, the functional plastics recycling for energy engineering spheres as technical system—is the whole of its external operating possibility: • • • •

human potential PL (t), technical potential PT (t), energy—material (plastic) potential PE (t), controlling potential PS (t). Function of operating potential:   Pd (t) = Φ P L (t), P T (t), P E (t), P S (t) ,

(2)

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147

The following ones belong to indicators describing the operating potential (the description is limited to controlling potential exclusively, as the basic concept tool of designer’s activity): • temporary course of real executive possibilities, π d (t) • volume of operation used actively, usefully M d (t) • theoretical possibilities and operations needs, ε, and especially: Pd (t) = πd (t) · Md (t) · ε.

(3)

Operating (energy) potential equation in the period [t0, T]: T Pd (T ) = Pd (to ) −

T pdE (t)dt

−

to

T pds (t)dt

+

to

pdo (t)dt

(4)

to

where: Pd (t o )—initial operating potential, pdE E(t)—density of effectively used stream of potential, pds (t)—density of lost stream of potential, pdo (t)—density of recovered (or obtained from the environment) stream of potential. Taking energetically plastics grinding for energy engineering aims into account we obtain: T Pem (T ) = Pem (to ) −

T E pem (t)dt

to

−

T s pem (t)dt

to

+

o pem (t)dt

(4a)

to

where: Pem (t o )—initial energy-material potential (e-m) of plastics grinding system, pEem (t)—flux density of effectively used e-m raw, plastics potential, psem (t)—flux density of wasted and lost e-m plastics potential, poem (t)—flux density of e-m recreated plastics potential, (or only retrieved from environment). The principle of the cognitive control support in the direction of getting the extreme solution can be defined:

    ∗ (5) X ∈ ϕ : ∧ Z (x) ≥ Z X ∗ , x∈ϕ

in the case of minimization of energy consumption (Z = eR or Pem (T ))

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 X∗ ∈ ϕ :



  ∧ Z (x) ≤ Z X ∗ ,

(6)

x∈ϕ

in the case of maximization of energetic milling indicator (Z = eR). If the target point is known in the target space (e.g. eR < 75 kJ kg−1 and/or eR > 60), it is possible to conduct the procedure aiming at approaching the given solution. The procedure means searching for such δS, δER which are expressed by the following formula: δS =

E Rmax − E Rmin Smax − Smin ⇒ 0, δ E R = ⇒ 0, Ssr E sr

(7)

This way a new objective function is obtained. It is in the form of the distance between the target condition and the countess condition in the target space: Z d (x) = Z min − Z (x),

(8)

where: Zd —the distance between the solution quality vector Z(x) and the target solution Zmin . In the case of Euclidean norm, the distance is expressed by the following formula:  Z dl (x) =



21 [Z imin − Z i (x)]2

,

(9)

i=1

where: Zimin —the value of unitary energy consumption for the target solution, Zi (x)—the value of the unitary energy consumption for the designed solution. The grinding process is described by a number of indicators such as: efficiency, reliability, outcome, product quality. Each indicator requires different sources of information. In the grinding engineering, there are three main groups of objects (Bieli´nski and Flizikowski 2013; Ministry of Economy 2009): • material that is considered in three states: – at input, – during mechanical processing, – at output, • machine, as a functional system that may include: – drive motor (motors), – mechanical gear,

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– grinding unit, • process considered with respect to categories related to: – – – – – –

energy, cost-efficiency, environmental aspects, organisational aspects, time, social aspects.

The above shows that grinding is a multi-stage process that requires detailed knowledge on properties of specific groups of objects: the material, the functional unit and the grinding process itself. Grinding process indicators. In the case of biomaterials recycling, the energy-related indicators that significantly affect the grinding process include (Flizikowski 2002, 2008; Flizikowski, Bielinski 2013; Macko et al. 2011; Powier˙za 1997; Tomporowski 2012a): • • • • • • • •

total energy of fracture propagation, crack stress, crack resistance, load during collision and cutting, collision duration, digestibility of organic material, performance ratio of ground product incineration, relation of dimensions before and after the grinding process, increase of specific surface area.

Many theoretical works known as theories have been published on the subject including theories by: Griffith, Behrens, Rumpf, Schonerta, Kerlin, Flizikowski. Figure 1 shows a schematic diagram showing variables, constants and disruptions taken into account during active monitoring of the grinding process. Choosing a strategy for active, cognitive monitoring and grinding of technical objects and must take into account models and objectives, for example, the highest efficiency of the grinding process and final product quality (e.g. optimal) (Bieli´nski and Flizikowski 2013): e E = f (Po , Pe , Ps , Pod , E j , Onq ) where: Po —initial potential, Pe —effectively used potential, Ps —ineffectively lost potential, Pod —potential recovered from technology or the surroundings, E j —unit energy consumption, Onq —low quality product, waste, loss, defect operations etc.

(10)

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THE SURROUNDINGS (ENVIRONMENT) TECHNOLOGICAL AND ENERGYRELATED NONRENEWABLE GOODS RAW MATERIALS

SAFETY OF SUPPLIES

GRINDER ST Czynniki stałe

Yot = Xst

Zmienne niezależne

Cg

TOP gran_C AQ

Cs NEEDS COMPENSATION

?P

Cm

ER

Cr

eR

? max

?S

Zmienne zależne

MATERIALS

TECHNOLOGICAL AND ENERGYRELATED RENEWABLE GOODS

SECONDARY GOODS SERVICES

MONITORING ZONE SM

Yst = Xot

SECONDARY GOODS PRODUCTS

Cz SUN

WASTE

Czynniki zakłócające

POWER

ENERGY

Fig. 1 General diagram of elements and relations between the grinding system (ST), the environment (OT), the monitoring cognitive zone (SM = SG) (boundary zone)

As a result of the development of modern IT technologies, active monitors, optimization numerical methods (statistical and deterministic) have displaced previously used analytical methods. The principle of operation of deterministic methods is to aim at the optimum value by using appropriate algorithms. The best results are obtained by an effective combination of both methods. The technical system (ST) includes the grinding unit (Fig. 2) connected directly by a drive shaft or through the gear to an electric motor (motors). Each drive motor is equipped with a dedicated inverter that controls its operation. The technical system also includes physical quantity measurement units and necessary systems: • for material transport and feeding, • wiring systems (power supply, measurement, control, data transmission, safety, visualization and alarm), • ground material collection, transport and packing system. The boundary zone (SG) is made up of programmes, algorithms, executive units, modules of active monitoring system (SM) used to identify, acquire, analyse, verify and exchange data and to configure the system, control inverters of motors that drive the grinding unit. This zone also consists of modules and tools used to analyse data that supplements the monitoring system, such as image identification, Genetic Algorithms, MS EXCEL, STATISTICA.

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THE SURROUNDINGS (ENVIRONMENT) TECHNOLOGICAL AND ENERGYRELATED NONRENEWABLE GOODS

MONITORING ZONE SM DRIVERS

DATABASE

REPORTS AND GRAPHS

EXPLORER

SECONDARY GOODS SERVICES

CONFIGURATION

RAW MATERIALS

SAFETY OF SUPPLIES

GRINDER ST NEEDS COMPENSATION

Xst

Yst =

On-line, Off-line

Yot =

On-line, Off-line

MATERIALS

TECHNOLOGICAL AND ENERGYRELATED RENEWABLE GOODS SUN

Xot

SECONDARY GOODS PRODUCTS

OTHER ANALYTICAL MODULES

POWER GENETIC ALGORITHMS

MS EXCEL, STATISTICA

DATA EXCHANGE

WASTE

CONTROL ENERGY

Fig. 2 Detailed diagram of elements and relations between the grinding system (ST), the environment (OT), the extended monitoring zone (SM = SG) (boundary zone)

When implementing a monitoring concept adopted to increase knowledge about the recycled and reuse materials grinding process, the following must be chosen as part of the technical infrastructure selection procedure: information sources, types of measured quantities, interval values for measurement and measured data recording, implementation of simple and complex algorithms for aggregation of data. Reference values must also be defined for further analysis and standard and custom reports on test results must be pre-defined, for those interested in achieving the highest efficiency of the grinding process.

3 Cognitive Motional Characteristics Cognitive, motional, usable characteristics and multi-disc and multi-hole grinding outcome variables: power demand (PR = f (n)), degree of fineness (λ = f (n)) and mass target efficiency (Qm = f (n), Qc ≤ Qm ) depend on the common area of the edges of two holes (S c , S T ), density and volume of grain in the working space (ρ m , V g ), rotational, angular and linear speed of a component and time (n, ω, v, Θ, t i ) − L(PR , λ, Qm , Qc ) = P(Sc , ST , ρ˜nm+1 , Vg¯ , n, ω, v, Θ, ti ); they also depend on the volumetric dosing of mass feed q(0;1). The usable characteristics of grinding, dependent on the movement of grain and grinder components were named the motional characteristics of grain (Flizikowski and Co-authors 2005; Flizikowski 2011a; Knosala and

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(a)

(b)

Fig. 3 RWT-5KZ multi-hole five-disc grinder working unit and space (Flizikowski 2011b; Tomporowski 2012b): a grain filling in two adjoining working holes of the quasi-cutting unit: Tn−1 do Tn+2 —subsequent grinding discs, hG —height of material column before the cutting plane, hD —height of material column behind the cutting plane, VG g —calculated volume of material before the cutting plane, VD g —calculated volume of material behind the cutting plane, SC —common area of the quasi-cutting pair of holes; b cross-section of the multi-disc unit: 1—bearing, 2—grinding disc (so-called “preceding” disc), 3—grinding disc (so-called “subsequent” disc), 4—body, 5—shaft, 6—pulley

Co-authors 2002; Niederli´nski 1987; Sidor 2006; Tomporowski and Opielak 2012; Zawada and Co-authors 2005; Ziemba et al. 1980). To the active, cognitive grinding monitoring, determine motional characteristics of reuse grain and grinder working unit, two states were assumed which are dependent on the linear speed of the grinding holes edges (Fig. 3) (Flizikowski 2011b; Tomporowski 2012b): the first one—idle state, involving only movement and mixing, exclusive of grinding (linear speed of points on edges—below 0.7 m s−1 ), second—working state, with significant grinding initiators (above 0.7 m s−1 ). The positioning of grains being ground in the working space of a multi-hole grinder (Fig. 3a) is described by statistical distribution of its length. Because the material present in the holes of this same disc is characterised by the same particle size ρ and is subject to the same grinding and cutting process in each hole, its state for the purpose of this analysis is indexed with the cutting number (m) and the disc number (n) (Tomporowski, 2011a, b): lmax ρnm

: [0, lmax ] → [0, 1],

ρnm dl = 1.

(11)

0

The thickness of the nth disc, for the purpose of this analysis, is marked with the symbol yn , and the height up to which the material fills the hole in the nth disc prior to the kth cut by the symbol y˜n(k) . In efficient grinding and cutting, length distribution of grains which filled the empty space in the (n + 1)th disc changes as per the following dependency (Fig. 3a):

Principle of the Cognitive Grinding of Reuse Materials

m ρ˜n+1 (x)

=

An,m ρnm

 = 1−

153

 lmax x 1 m ρnm (l)dl, ρn (x) + m m yn+1 − y˜n+1 yn+1 − y˜n+1 x

(12) while of those left in the nth disc as per:

ρ˜nm+1 (x)

=

B˜ n,m ρnm

  lmax x 1 m = 1 − m ρn (x) + m ρn (l)dl, y˜n y˜n

(13)

x

where: A, B—stochastic operators for mth cut, nth disc. It was assumed for simplification purposes that, subsequent to grinding, distribution of granulated product in the hole spaces of the (n + 1)th disc will be uniform (cut fraction and that present in the hole before cutting will mix) and it will therefore k be the weighted average from ρn+1 i ρnk : m ρn+1 (x) =

m m yn − y˜n+1 y˜n+1 m−1 ρn+1 + An,m ρnm (x) yn+1 yn+1

(14)

During the modelling of the common part surface, integration of grinding momentary cross-section was employed (Flizikowski and Co-authors 2005; Flizikowski 2011b; Tomporowski 2011b): x2  x2    1/2   2  2 1/2 b2 + R2 − (x − a2 ) dx − b1 − R12 − (x − a1 )2 dx SC = x1

x1

(15) where: a1 , a2 , b1 , b2 —C1 and C2 hole centres coordinates, R1 , R2 —holes radius vector. Based on what has been said, distribution of grain length in ground material which filled the empty space of the (n + 1)th disc changes as follows:

m ρ˜n+1 (x)

=

An,m ρnm

x 1 = (1 − D )ρnm (x) + D h h

lmax ρnm (l)dl

(16)

x

whereas in the material left within nth disc in the following way (analogical reasoning):

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ρ˜nm+1 (x)

=

B˜ n,m ρnm

x 1 = (1 − D )ρnm (x) + G h h

lmax ρn (l)dl

(17)

x

It must be remembered that the column of the material being cut is not the entire material that has been moved to the lower hole. Its volume is: S c · hD whereas that of the entire material moved from the preceding hole to the subsequent hole is VgD (αc ) − Vnm . It means that the second and third component in (16) must be multiplied by the relation between these volumes: ρ˜nm+1 (x) = B˜ n,m ρnm = (1 −

Sc x m m )ρn (x) VgD (αc ) − Vn+1

Sc + D m Vg (αc ) − Vn+1

lmax ρn (l)dl,

(18)

x

It was assumed for simplification purposes that, subsequent to cutting, the grain length distribution in the (n + 1)th disc will be uniform (cut fraction and that present in the hole prior to cutting will mix), and therefore it will be the weighted average m−1 m−1 from ρn+1 and ρ˜n+1 . m ρn+1 (x) =

m m Vn+1 V D − Vn+1 m−1 ρ + Bn,m ρnm (x) n+1 VD VD

(19)

The filling level of the quasi-cutting unit and thus the efficiency of the cutting process depend on the value of the function V D , V G and S c which in turn depend on the direction of the effective gravitation and on the total volume of material in both m + Vnm ). holes before cutting (Vn+1 Other theory, models and indicators of variables are provided based on optimization tests. With the stabilised motion of grinding components ((n, ω, v) = const), for the analysed scope of polymer waste granulate movement and with the use of functional models (unit energy consumptions and modified Froude number), it is possible to acquire knowledge of and design mutual relations within the multi-hole and multidisc system of the grinder working unit: filling of the transport and grinding area, power, process target and mass efficiency as well as linear (circumferential) and rotational speeds of operating discs (Fig. 4).

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Fig. 4 New structure of grinding conditions W: PP-start and products material features Wm , grinding process features Wp , and design of machine elements Wd (Openings, holes = 5, = 10)

4 Cognitive Compensation of the Technical System Figure 5 shows a block diagram representing the operational algorithm of the cognitive measurement system of the environmental optimization of the recycled and reuse grinding process (compensatory monitoring of the energy/material parameters of the grinding process). On the basis of this algorithm, the system design

Fig. 5 The algorithm of cognitive compensatory monitoring of energy/material parameters of special grinding systems

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is presented: the process structural system (KP)—technical and control structural system (KC)—that handles the system input. The system of compensatory monitoring of the energy/material parameters of the grinding process was created on the basis of an IT concept that consists in the centralization of all measurement data and their calculation in a single database, with the application that manages the system works in client-server arrangement. Monitoring the energy/material parameters of the environmental recycled and reuse materials grinding (Fig. 5) is a precondition for the understanding the dynamics of the key phenomena occurring in special disc grinders (KP). At the same time, measurement data recorded in the database of the system may be used as input data for advanced analytic (genetic) algorithms, whose purpose is to generate signals that automatically control (KJ) the operation of the grinder. A concept was developed and a system built for monitoring energy/material parameters of grinding.

5 Solutions of Cognitive Grinding It was assumed in the experimental verification that the trajectory of grain moving in the area between the radial lines of the external and internal cone (holes positioning envelope) is a continuous line—in terms of movement and mixing (first state) and continuous and disrupted (for the duration of grinding)—in the case of the second state (efficient edge speed, for e.g. corn: vR > 0.7 m s−1 ). As the speed of grains during movement and mixing is low and because grains are stopped for the duration of quasicutting and there is a potential increase in the flying movement of grain specks as a result of bouncing subsequent to grinding, it can be assumed for simplification purposes that the second state has the motion trajectory and time equal to t p+m+r = t p+m . Figure 5 illustrates the operation of the solution for cognitive grinding, active monitoring energy/material characteristics and parameters of the grinding process within the target structure. The basic design of each monitoring system includes an object layer, a data concentration and registration layer, a computer and data transmission layer, and application layer including a database and an application managing the system. An object layer consists of motors (KC) that drive grinder discs (KP) and a system for power electronic control and measurement of physical parameters (KC = KJ) (Flizikowski 2011a; Zawada and Co-authors 2005; Ziemba et al. 1980). The machine—5-disc recycled and reuse materials grinder (KP) is cognitive controlled, regulated and compensated by a system of pDrive inverters (KC = KJ) installed in one control cabinet—SR 230/400 V 63A IP44 (KL) with the controller (KC) of the DSK material feeder (KL).

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6 Solutions and Results Analysis With the cognitive stabilised motion of grinding components ((n, ω, v) = const), for the analysed scope of plastics grain movement and with the use of functional models, it is possible to acquire knowledge of and design mutual relations within the multi-hole and multi-disc system of the grinder working unit: filling of the transport and grinding area, power, process target and mass efficiency as well as linear (circumferential) and rotational speeds of operating discs. The systematised environment and technical system characteristics of idle running and load, based on specialist calculations and investigations into recycled and reuse materials grinders, indicate that the filling of the quasi-cutting unit, and therefore efficient, power demand and energy consumption in the granulate/re-granulate cutting process, depend on the values of operating speeds of both quasi-cutting and feeding. These, in turn, depend on motional and drive parameters of individual grinding units and the sum of momentary cross-sections, material volume in adjacent holes, making up a grinding unit. The grain and granulate motion characteristics, at various stages of disintegration and movement, depend on the common areas of the preceding and subsequent holes and their filling level with material both before and behind the cutting plane. Applying a practical approach related to the structure and operation of recycling machines–assuming a environment and design solutions (as a logical conjunction of criteria and structural features of a quasi-cutting unit) within the conceptual space, providing an optimal solution from the point of view of the selected criteria including objective, minimum power, auto-adjustment and multi-level structure–it is possible to propose a new pro-developmental solution with regard to further analyses of the integrated grinding system in the field of permissible variability of structural features and processing parameters.

7 Conclusions The methodology of calculations and examination of the characteristics of recycled and reuse granulate, grain motion, for idle and loaded grinding, may lead to improvement and development of processing machines. The active, cognitive grinding, monitoring selected characteristics of grain motion point to the need for reaching a compromise between the two basic functions: movement and grinding within the intra-hole working space. Proposed and partly verified models will facilitate selection of optimal structural features and multi-disc grinding process parameters. It is a useful and desired course, resulting ultimately in obtaining a high energy product with a defined form, structure and repeatable dimensions. The analysis of the current studies and structural basics of PVC-, PP- and PE-waste grain grinders, as well as detailed mathematical descriptions of the grinding process in relation to the structure of disintegrating units confirm the possibility of development

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and experimental verification of mathematical models useful for cognitive control, optimisation (modernisation and advancement) of multi-disc grinding structures. Models and corresponding mathematical dependencies facilitate efficient designing and planning of multi-hole grinding systems utilisation. For the purpose of the design of special cognitive systems machines, devices and lines for recycled and reuse materials (biological and recycles corn) grinding and the compilation of statistical data, procedures for active, environmental and compensatory monitoring of grinding parameters were proposed. The algorithm that enables identification of: product quality in technology; control based on product quality indicators, the actual state of effectiveness of mechanical processing and harmlessness of the energy system effects (in CO2 emission equivalent units: 960.2 MgCO2 /GWh of saved electric energy). Procedures, based on the active, cognitive monitoring algorithm, were used to support innovation of creative activities, i.e. environmentally friendly actions, that is those actions that produce specific positive environmental results in the energy-related, clean, recycling mechanical processing of recycled and reuse materials and other energy corn materials. Cognitive controlled technologies and activities lead to standards described as: clean processes, high quality materials and products, effective use of energy in operations, and energy saving. The analysis concerned a multi-disc grinder of environmentally enhanced design at various stages of development including: industrial testing stage, prototype testing stage, certification stage, implementation and mass production planning (developmental implementation). For the purpose of rough evaluation and compensation of structural system operational parameters, the basic criteria of eco-innovation were applied: energy consumption, material consumption, operational efficiency and effectiveness, the overall waste balance (waste balance, waste produced).

References Flizikowski JB (2002) Dissertation on construction, [in Polish]. ITEE, Radom, Poland Flizikowski JB (2008) Plastic devices for energy engineering. NANOENERGY, Tapis Rouge, Paris, France Flizikowski JB (2011a) Intelligent grinding system. In: Chemical engineering and equipment no 3/2011. SIGMA-NOT Sp. z o.o., Warsaw, Poland, pp 22–23 Flizikowski JB (2011b) Levels of intelligent grinding system. In: Chemical engineering and equipment no 3/2011. SIGMA-NOT Sp. z o.o., Warsaw, Poland, pp 24–26 Flizikowski J, Bielinski K (2013) Technology and energy sources monitoring: control, efficiency, and optimization. IGI GLOBAL USA 2013; ISBN13: 9781466626645, ISBN10: 146662664X, EISBN13: 9781466626959, pp 248 Flizikowski JB, co-authors (2005) Implementation project of intelligent system in special mills construction. MNiI, ATR-AGH, Warsaw, Bydgoszcz, Krakow, Poland Knosala R, Co-authors (2002) Application of artificial intelligence in production engineering [in Polish]. Warsaw, Poland Niederli´nski S (1987) System and control, [in Polish]. PWN, Warsaw, Poland Ministry of Economy (2009) Polish energy policy until 2030. Warsaw, Poland

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Macko M, Boniecka M, Drop A (2011) Life cycle assessment of grinders rising SoliWorks sustainability application, [in Polish]. Chemical engineering and equipment no 3/2011. SIGMA-NOT Sp. z o.o., Warsaw, Poland, pp 49–50 Powier˙za L (1997) Outline of bioagriculture engineering system, [in Polish]. Part I. ITEE, Radom, Poland Sidor J (2006) Studies, models and methods of designing vibratory mills, [in Polish], vol 150. Publisher AGH ROZPRAWY MONOGRAFIE, Krakow, Poland Tomporowski A (2011a) Structure development of biological material shredders. Part I and II [in Polish]. Chemical Engineering and Equipment no 3/2011. SIGMA-NOT Sp. z o.o., Warsaw, Poland, pp 75–78 Tomporowski A (2011b) Effectiveness of drive and innovative construction solutions of multidisc grinders for biomass grain—case study, [In Polish]. LTN, Lublin, Poland Tomporowski A (2012a) Filling model for the working multi-disc biomass grain grinding unit. In: The archive of mechanical engineering, vol LIX, no 2. Poland: Polish Academy of Science, Committe of Machine Design, Versita, Warsaw, pp 155–174 Tomporowski A (2012b) Stream of efficiency of rice grains multi-disc grinding. Maintenance Reliab 14(2):150–153 Tomporowski A, Opielak M (2012) Structural features versus multi-hole grinding efficiency. Maintenance Reliab 14(3):223–228 Zawada J, Co-authors (2005) Introduction to mechanics of machine crumbing process, [in Polish]. ITE, Radom-Warsaw, Poland Ziemba S, Jarominek Wł, Staniszewski R (1980) Problems of systems theory, [in Polish]. Ossolineum, PAN, Wrocław, Poland

Smart-Tracking Systems Development with QR-Code and 4D-BIM for Progress Monitoring of a Steel-Plant Blast-Furnace Revamping Project in Korea H. Y. Roh, E. B. Lee , I. H. Jung, and C. Y. Kim Abstract Blast furnace revamping in the steel industry is one of the most important works to complete the complicated equipment within a short period of time-based on the interfaces of various types of work. P company has planned to build a Smart Tracking System based on the wireless tag system to comply with the construction period and reduce costs, ahead of the revamping of blast furnace scheduled for construction in February next year. It combines detailed design data with wireless detection technology to grasp the stage status of design, storage, and installation. Then, it graphically displays the location information of each member to the plan and the actual status in connection with Building Information Modeling (BIM) 4D Simulation. QR Code is used as a wireless tag to check the receiving status of core equipment considering the characteristics of each item. Then, DB in the server system is built, status information is input. By implementing BIM 4D Simulation data using DELMIA, the information on location and status is provided. In terms of logistics digitization, the system’s features allow suppliers to monitor the real-time status of the 4D system provided on a Web basis, enabling suppliers to accurately identify and supply the delivery times for the facilities they need to manufacture and supply. Besides, the project progress control can be managed quickly and accurately by identifying the location of major delivered facilities in real-time to maximize the efficiency of construction, and by identifying the status information of major facilities, i.e. installation status in real-time. This digitalization ultimately results in savings in the manpower involved in the project and contributes to lower investment costs as a whole. Keywords Wireless tracking system · Blast furnace revamping · BIM 4D · Real-time detection of facilities

H. Y. Roh · E. B. Lee (B) · I. H. Jung · C. Y. Kim Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Korea e-mail: [email protected] © Springer-Verlag GmbH Germany, part of Springer Nature 2020 P. Golinska-Dawson (ed.), Logistics Operations and Management for Recycling and Reuse, EcoProduction, https://doi.org/10.1007/978-3-642-33857-1_9

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1 Introduction An active wireless recognition system (based on QR-code) in a 4D-BIM environment has been proposed. In connection with the new aging of the population, the construction period of the mid-term facility replacement business is increasing due to the aging of construction personnel and the lack of skilled workers. As a result, productivity is expected to decrease naturally. This deterioration is expected to be more severe in blast furnace repair. Steel plant features consist of various facilities and construction processes (instrumentation, machinery, electricity, civil engineering, and construction). In other words, it is composed of various equipment processes, so it takes a lot of time and money to rework when errors and corrections occur. Therefore, it is essential to use primary data for IT-based system data accumulation and reproducibility analysis to prevent design and construction errors and to reduce costs based on technology accumulation. In this chapter, we propose a design and construction system that integrates wireless tracking technology in a BIM environment for a new concept of technical grafting. The main goal of the system is to improve consistency through maintaining data consistency at the design-purchase-construction stage in the EPC engineering stage and eliminate errors by linking with the BIM technology. The proposed system can input step data for equipment in real-time after the production of the product by the manufacturer. In the case of the yard, it has been input the data for four areas for the location and checks the detailed material information through tagging in each step to prevent errors due to crosstalk. Furthermore, the 4D viewer can check the status and location of the facility in real-time, and can check the preparation status of the target facility in advance in connection with the 4D Schedule, and has the advantage of easy real-time tracking. With these advantages, problems of scheduled work can be identified in advance to minimize errors.

2 Related Work 2.1 Conventional Wireless System Technology For the efficient and automatic inventory management system of logistics and distribution, we mentioned the need for wireless tagging technology. Several examples of wireless tagging-based technology are introduced, and methods using RFID or Bluetooth are introduced. First, for inventory automation of outdoor warehouses, we proposed a system that tracks and manages products with unmanned drones by attaching Bluetooth Low Energy (BLE) and beacon technology to products. However, the results of this study needed to derive actual results for the problem of beacon distance recognition according to the drone’s route (Bae et al. 2018). Similar technologies have been introduced mainly for logistics management and indoor locationbased system research of smart factories based on Bluetooth beacon communication (Am-Suk 2015; Park et al. 2015). Also, the business model components are organized

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by classifying the cases of presenting the To-Be model for companies participating in the international logistics process using RFID wireless tagging technology (Choi et al. 2010; Jang et al. 2007) and examples for the activation of the RFID technology industry. There are techniques presented (Joung and Kim 2013). Besides, a technology that automatically updates product information to a database using a mobile-robot and an unmanned mobile robot based on RFID wireless communication was introduced (Son and Do 2011).

2.2 The Advantages of Proposed System In this paper, it has been introducing the technology to visualize products in the 4D BIM environment of the process based on the QR code wireless tagging technology. The advantages of QR codes can be widely used in manufacturing, distribution, logistics, and marketing. On-site product information and ordering information are automatically converted into digital systems by QR code recognition, which is very easy to configure as a database. The converted database can be used to order and inspect products, and it is easy to collect and track information about products sold or received. This advantage of QR code helps you to search for distribution and distribution systems effectively and quickly. In other words, it can accurately provide users with information about products sold or received (Park 2013; Gu and Zhang 2011; Yoon et al. 2011; Dey 2013; Goyal et al. 2016; Bhargava et al. 2014; Ramsden 2008). The QR code can also be used for construction management and informs workers, including construction and construction procedures so that they can quickly search for construction-related information. Additionally, worker information may include the company the worker belongs to, whether safety training has been completed, and the extent of the approved work. To this end, a QR code is attached to the worker’s helmet so that the manager’s work information can be checked immediately. Due to the attachment of the QR code, the construction manager does not have to visit the construction site directly with the construction documents and has the convenience to check the current status of the work (Lorenzo et al. 2014). Where, the smart tracked product information is stored as a central server that manages the BIM platform and database, and the visualization result is derived based on the data stored in the server. The derived result can be accessed in the form of a separately developed UI (User Interface) for the end-user. Central server access through the designed UI can be accessed using a smartphone or PC. That is, based on the IP allowed access to the central server, you can access anytime, anywhere, through a device that can use the Internet. This enables process managers to have very convenient and efficient management in process control and supervision.

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3 Smart Tracking System 3.1 The Wireless System Modeling Product QR code information recognition of the proposed system captures product QR code information in the form of a smartphone device or mobile-robot in the field. The captured data is transmitted to the central server and stored through the Web, and can be linked to the 4D BIM environment at the process site through the CM (Construction Management) Platform. Where, the QR code is scanned based on the QR code data collected and taken. If the target QR code is treated as unconfigured data, it is switched to the main screen with a search failure notification. On the other hand, once the QR code is recognized, it is divided into an input-capable case and an input-incapable case according to the accepted QR code classification. An example of a classification system for identified data is a process-related data classification system such as delivery, receipt, storage, equipment, and inspection. And the overall system configuration of the proposed system is shown in Fig. 1. The applicable range of the designed system is the EP equipment, the main unit, the furnace, and the gas cleaning class supply EP equipment for the 1st runner of furnace facility. QR code is attached to the recognition object, and the QR code information is DB. Afterward, a UI-type QR-code operation screen is provided for end-users. The following figure is an example of the main screen of the developed UI (Fig. 2). Where, the definition of each function is as follows: • • • • •

A: Project information B: Facility information C: Supplier information D: Function button for each status E: Search and Exit button.

Fig. 1 The proposed system

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Fig. 2 The main screen of user interface

And it can check the delivery status of the product by date through the next screen in more detail (Fig. 3). • • • •

A: B: C: D:

Unstoring date and status Unstoring button Warehousing date and status Warehousing button.

Fig. 3 The main screen of delivery status of the product

166 Table 1 The required data list

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Property

Data

Project information

Project period

Contact number of P company

Project name

Contact number of contractor

Contractor

NULL

Equipment information

Status information

Item name

Count

Picture of item

Weight

Packing tracking number

Supply flag

Tag code

Supplier name

Class number

Packing number

Drawing number

Contact number of supplier

Un-storing date

Install status

Un-storing status

Installation location

Warehousing date

Measurement date

Warehousing status

Measurement status

Open storage date

Installation completion date

Storage location

Installation completion status

Install date

Install status

In other words, by using the provided interface, you can conveniently check the progress and schedule of the entire process, and track the status of the operator. Where, the contact number is used when contacting the P company’s project management (PM), construction management (CM), or business personnel. Project information indicates information about construction information. The construction manager can quickly identify documents by construction by checking the document number referring to database information. Information such as product quantity, weight, and drawing drawings of the product are useful when checking whether the product information matches the order. Besides, the status information includes the progress status of each construction job and the date showing the history. The main entities derived from the inputs listed in Table 1 are project equipment and contractors. The main objects are project equipment and contractors. For security reasons, if the user scope is restricted, a user entity is added, and the figure below shows the relationship structure according to the substance. In Fig. 4, the red attribute is primary, and the information marked with green sequel means the foreign key. And the interface table is implemented based on the diagram in Fig. 4.

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Fig. 4 The diagram of entities relationship

3.2 The 4D BIM Simulation When the database configuration work is completed in the interface table through the wireless system modeling work, the collected items are periodically uploaded to the existing BIM platform DB. In order to link the useful tracking function with the 4D BIM environment, 3D modeling work must be preceded. The basic format of 3D modeling work is derived as a 3D XML file and is additionally compatible with data such as.dxf and.dwg (Dassault system based modeling). As mentioned earlier, in this study, the smart tracking system is integrated into 3D and 4D environments to provide users with information on construction items quickly. Therefore, the drawing status information uploaded to the BIM platform database must be changed to color information. For this, the essential functions of B. I (Business Intelligence) supported by the 3D experience application are used. B. I function helps construction managers easily identify the necessary information (Bernérus and Karlsson 2016). By utilizing the B. I. function, 3D models can be displayed in a predetermined color on the open storage at each stage of installation completion. To link the status information of each item to the 3D model, you need to connect the 3D model from the BIM database with the smart tracking system and tag data used to specify the product. The recommended process is to provide the generated code in advance to the 3D model according to pre-specified rules when creating a 3D model. That way, you can assign tag codes to many 3D model objects without having to find something to manage. The 3D model

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list is preferentially extracted and displayed as color information, and experts link tag data to the list. And the final list connected by experts consists of a database inside the interface table.

4 Experiment 4.1 Wireless System and User Interface When the proposed system was applied to an actual construction site, an experiment was conducted to check the efficiency. The experimental method goes through the procedure where the construction site user utilizes a smartphone to recognize the QR code and configure the known QR code as a database in the interface table. As mentioned above, any device that can use the web environment does not matter. When a database recognized as a wireless system is configured, the 3D modeling database is also set on the same server in the manner mentioned in Sect. 3. When the databases required for the experiment are configured, the engineer can check whether the smart tracking function and data configuration procedure are correctly performed in the server environment and the reliability of the set database. For the experiment, there are 3D experience licenses and some necessary provisions, and the table below lists them. After the necessary provisions in Table 2 were all composed of data in the interface table, the results of the reliability verification experiment were confirmed, and the figures below show the experimental results. Figure 5 shows the databases organized in the interface table (job information, product information, construction schedule, wireless system tagging information, etc.). And Fig. 6 shows the progress of a specific construction schedule (visualized in the form of a bar at the top of the UI) and connection attribute information with construction manager information. Looking at Fig. 5 first, the QR code and product information are visualized when the active Downloads button on the right tab of the Table 2 Check list Division

Description

Wireless system

Connection via QR code Direct connection not through QR code Check of Item search result Entering status information according to scenario and check of result Functionality of main page

Equipment information

Display of status information of items in 3D model Check of location information of item in 3D model Display of status information of items in 4D model

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Fig. 5 The result of interface table data check

screen is pressed, based on the QR code captured by the smartphone in the field. If it is the job information, the status information window for the job is visualized, and if it is the job worker information, the necessary knowledge of the worker and whether or not the job safety training is completed are visualized. By checking the derivation results in Fig. 5, the job manager can quickly and accurately review and correct the site conditions and progress of construction work. Next, the results of Fig. 6 are first visualized the progress of the construction work in the form of a bar at the top, and it can be confirmed that the call connection function is activated by recognizing the connection information with practitioners for each operation. In other words, the general manager in charge of construction reduces the task of separately managing contact information of each worker, so that it is possible to control the work process schedule quickly and accurately. When the proposed system is applied to the site through the results of experiments, hundreds, thousands of items are targeted, unlike experiments involving 13 issues. The proposed smart tracking system is a concept of a pilot-test performed before application to the construction site. And the site application procedure is as follows: • (First): The items to be managed are selected from all items in the construction project. • (Second): Set the product details for the product to be handled. Here, if issues are managed in too small or large units, it is difficult for the user to grasp the current status of each item, so it is necessary to select an appropriate group.

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Fig. 6 The result of system progress and task manager call connection information check

• (Third): QR codes are distributed to suppliers of things to be managed, and tags with QR codes are attached to each item. In this case, the attachment method is attached in such a way that it can be maintained during the maintenance period, taking into account characteristics such as how to use, size, or environment of each product. • (Fourth): Typically, tags printed on paper are used due to price advantages, but for items placed in extreme situations, use a QR code lasered on an aluminum plate to prevent the tag from falling or being damaged. • (Fifth): Suppliers scan tickets and update item status on delivery. • (Sixth): Experts with sufficient knowledge of each item connect the 3D model to the tag data of the target item to construct the final interface table database.

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Fig. 7 The result of the mechanical drawing applied to the experiment

4.2 Mechanical Drawing To visualize in a 4D BIM environment, 3D modeling work is required in advance. In this paper, 3D modeling was performed based on the blast furnace drawing of the Korean steel company P, and Fig. 7 shows the modeling results. Where, the left side of Fig. 7 shows the attributes for the mechanical drawing elements, and the upper right tab indicates the status for each color. The parts marked in dark pink for each color represent securely separate elements. White is the current process. Yellow and dark blue show the shipping and loading status of the process product, respectively. Orange means the yard, and purple color is the assembly completion process. Dark green indicates the installation status; light pink indicates the completion status, and finally, bright green indicates the installation status. The drawing results in Fig. 7 represent each color based on the process data collected by the wireless system and stored in the interface table database. In other words, by checking the process status with drawings, data add-on is possible in the 4D BIM environment.

5 Conclusion In this paper, we proposed a smart tracking system using QR code-based wireless tagging technology. The purpose of the proposed method is to reduce the cost and reduce working hours due to the decrease in worker productivity in construction or various industrial sites by introducing smart construction technology. Furthermore, by using this system, project managers were able to manage projects effectively. As an example, it was confirmed that the final administrator could access the project through

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the user interface environment by constructing the interface table inside the central server and providing the user interface environment. Besides, this approach was able to confirm the construction project’s progress by-product, construction work, worker, product release, product receipt, and product shape using the Web. The utilized results were verified through experiments and summarized the necessary processes until the results of the verified pilot-tests were applied to industrial sites. The proposed smart tracking system will be gradually introduced into the blast furnace process, which has been tested, until next year. Also, the proposed intelligent tracking system architecture will be applied universally beyond the blast furnace process involved in the experiment. Due to this, in the future, the smart process system can be expected to decrease worker productivity due to the aging population and ensure efficient work for managers. Funding Acknowledgements The authors acknowledge that this research was sponsored by (1) the Korea Ministry of Trade Industry and Energy (MOTIE/KEIT) through the Technology Innovation Program funding for (1) “Artificial Intelligence Big-data (AI-BD) Platform for Engineering Decision-support Systems (grant number = 20002806)”, and (2) POSCO for “Smart-Tracking of Construction Progress for Blast-Furnace Retrofit based on 4D BIM”.

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