City Logistics Series Editor Jean-Paul Bourrières City Logistics Modeling and Planning Initiatives Edited by Eiichi Taniguchi Russell G Thompson First published 2018 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd 27-37 St George’s Road London SW19 4EU UK John Wiley & Sons, Inc 111 River Street Hoboken, NJ 07030 USA www.iste.co.uk www.wiley.com © ISTE Ltd 2018 The rights of Eiichi Taniguchi and Russell G Thompson to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988 Library of Congress Control Number: 2018936342 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-78630-206-9 Contents Preface xv Chapter Urban Logistics Spaces: What Models, What Uses and What Role for Public Authorities? Danièle PATIER and Florence TOILIER 1.1 Introduction 1.2 Literature review 1.3 ULS typology 1.3.1 The Urban Logistics Zone (ULZ) or freight village 1.3.2 The Urban Distribution Center (UDC) 1.3.3 Vehicle Reception Points (VRP) 1.3.4 Goods Reception Points (GRP) 1.3.5 The Urban Logistics Box (ULB) 1.3.6 Mobile Urban Logistics Spaces (mULS) 1.4 Recommendations 1.5 Conclusion 1.6 Bibliography Chapter Dynamic Management of Urban Last-Mile Deliveries Tomislav LETNIK, Matej MENCINGER and Stane BOZICNIK 2.1 Introduction 2.2 Review of urban freight loading bay problems and solutions 2.3 Information system for dynamic management of urban last-mile deliveries 2.4 Algorithm for dynamic management of urban freight deliveries 2.5 Application of the model to a real case 2.6 Conclusions 2.7 Bibliography 4 12 13 15 18 19 20 23 23 25 26 29 32 33 34 vi City Logistics Chapter Stakeholders’ Roles for Business Modeling in a City Logistics Ecosystem: Towards a Conceptual Model Giovanni ZENEZINI, J.H.R VAN DUIN, Lorant TAVASSZY and Alberto DE MARCO 3.1 Introduction 3.2 Research background 3.2.1 Business model concept 3.2.2 Business ecosystem 3.2.3 Role-based networks and ecosystems 3.3 The CL business model framework: roles, business entities and value exchanges 3.4 City logistics concepts and role assignment 3.4.1 Parcel lockers installation: MyPUP 3.4.2 Urban consolidation centers 3.4.3 Business model implications 3.5 Conclusions 3.6 Bibliography 39 41 41 42 43 43 48 48 51 54 55 56 Chapter Establishing a Robust Urban Logistics Network at FEMSA through Stochastic Multi-Echelon Location Routing André SNOECK, Matthias WINKENBACH and Esteban E MASCARINO 4.1 Introduction 4.2 Strategic distribution network design 4.2.1 Distribution network 4.2.2 Network cost 4.2.3 Distribution cost 4.2.4 Optimization model 4.3 Solution scheme 4.3.1 Scenario generation and selection 4.3.2 Design generation 4.3.3 Design evaluation 4.4 Case study 4.4.1 Data and parameters 4.4.2 Analysis results 4.5 Results 4.5.1 Design generation 4.5.2 Design evaluation 4.5.3 Sensitivity to cost of lost sales 4.6 Conclusion 4.7 Bibliography 39 59 59 62 63 63 64 65 67 67 68 68 68 69 70 71 71 72 73 75 75 Contents Chapter An Evaluation Model of Operational and Cost Impacts of Off-Hours Deliveries in the City of São Paulo, Brazil Cláudio B CUNHA and Hugo T.Y YOSHIZAKI 5.1 Introduction 5.2 Literature review 5.3 Proposed approach 5.4 Scenario generation 5.5 Results 5.6 Concluding remarks 5.7 Bibliography Chapter Application of the Bi-Level Location-Routing Problem for Post-Disaster Waste Collection Cheng CHENG, Russell G THOMPSON, Alysson M COSTA and Xiang HUANG 6.1 Introduction 6.2 Model formulation 6.3 Solution algorithm 6.3.1 Genetic Algorithms 6.3.2 Greedy Algorithm 6.3.3 Simulated Annealing 6.4 Case study 6.4.1 Case study area 6.5 Result analysis 6.5.1 Models comparison 6.5.2 Sensitivity analysis 6.6 Conclusion 6.7 Bibliography Chapter Next-Generation Commodity Flow Survey: A Pilot in Singapore Lynette CHEAH, Fang ZHAO, Monique STINSON, Fangping LU, Jing DING-MASTERA, Vittorio MARZANO, and Moshe BEN-AKIVA 7.1 Introduction 7.2 Integrated commodity flow survey 7.2.1 Overview 7.3 Key survey features 7.3.1 Sampling related supply network entities 7.3.2 Multiple survey instruments leveraging sensing technologies 7.3.3 A unified web-based survey platform vii 79 79 81 84 87 90 94 94 97 97 99 104 104 105 106 106 106 109 109 111 113 114 117 117 119 119 121 121 121 122 viii City Logistics 7.4 Pilot survey implementation 7.4.1 Sample design and recruitment 7.4.2 Shipment and vehicle tracking methods 7.4.3 Pilot survey experience and lessons learnt 7.4.4 Preliminary data analysis 7.5 Conclusion 7.6 Acknowledgements 7.7 Bibliography Chapter City Logistics and Clustering: Impacts of Using HDI and Taxes Rodrigo Barros CASTRO, Daniel MERCHÁN, Orlando Fontes LIMA JR and Matthias WINKENBACH 8.1 Introduction 8.2 Methodology 8.2.1 Principal component analysis 8.2.2 K-means clustering 8.3 Results 8.4 Conclusion 8.5 Bibliography Chapter Developing a Multi-Dimensional Poly-Parametric Typology for City Logistics Paulus ADITJANDRA and Thomas ZUNDER 9.1 Introduction 9.2 Literature review 9.3 Methodology 9.4 Evaluation and analysis 9.4.1 Inventory of all EU projects 9.4.2 Inventory of typologies 9.4.3 Land use typologies 9.4.4 Measure typologies 9.4.5 Urban freight markets 9.4.6 Traffic flow typology 9.4.7 Impacts 9.4.8 Gaps 9.5 Validation and enhancement of the inventory 9.6 Proposed typology 9.6.1 Approach 9.6.2 Dimension: Why? 123 124 125 126 127 129 129 130 131 131 133 135 135 135 140 140 143 143 144 145 146 146 147 148 149 151 152 153 153 154 155 155 157 Contents 9.6.3 Dimension: Where? 9.6.4 Dimension: Who? 9.6.5 Dimension: What? 9.6.6 Dimension: How? 9.7 Reflections 9.8 Conclusion 9.9 Acknowledgements 9.10 Bibliography Chapter 10 Multi-agent Simulation with Reinforcement Learning for Evaluating a Combination of City Logistics Policy Measures Eiichi TANIGUCHI, Ali Gul QURESHI and Kyosuke KONDA 10.1 Introduction 10.2 Literature review 10.3 Models 10.4 Case studies in Osaka and Motomachi 10.4.1 Settings 10.4.2 Results 10.5 Conclusion 10.6 Bibliography Chapter 11 Decision Support System for an Urban Distribution Center Using Agent-based Modeling: A Case Study of Yogyakarta Special Region Province, Indonesia Bertha Maya SOPHA, Anna Maria Sri ASIH, Hanif Arkan NURDIANSYAH and Rahma MAULIDA 11.1 Introduction 11.2 Theoretical background 11.2.1 Urban distribution center 11.2.2 Decision support system of city logistics 11.3 The proposed decision support system 11.3.1 System characterization 11.3.2 The logical architecture 11.3.3 Agent-based modeling (ABM) 11.3.4 Model verification and validation 11.4 Example of application: the case of Yogyakarta Special Region 11.5 Conclusion 11.6 Acknowledgements 11.7 Bibliography ix 157 158 158 159 159 160 160 160 165 165 166 166 168 168 170 175 176 179 179 182 182 183 184 184 185 187 190 191 192 193 194 x City Logistics Chapter 12 Evaluating the Relocation of an Urban Container Terminal Johan W JOUBERT 12.1 Introduction 12.2 Methodology 12.2.1 MATSim 12.2.2 Initial demand 12.2.3 Alternative scenarios 12.3 Results 12.3.1 Directly affected vehicles 12.3.2 Extended effects 12.4 Conclusion 12.5 Acknowledgements 12.6 Bibliography Chapter 13 Multi-Agent Simulation Using Adaptive Dynamic Programing for Evaluating Urban Consolidation Centers Nailah FIRDAUSIYAH, Eiichi TANIGUCHI and Ali Gul QURESHI 13.1 Introduction 13.2 Literature review 13.2.1 Evaluation models for city logistics measures 13.2.2 ADP for evaluating city logistics measures 13.3 Models 13.3.1 Freight carrier’s MAS-ADP model 13.3.2 Freight carrier’s MAS Q-learning model 13.3.3 Vehicle routing problem with soft time windows (VRPSSTW) 13.4 Case study 13.5 Results and discussions 13.5.1 Case (base case) 13.5.2 Case 13.6 Conclusion and future work 13.7 Bibliography 197 197 199 199 200 201 201 202 205 208 209 209 211 211 212 212 213 214 215 217 218 220 221 222 223 226 226 Chapter 14 Use Patterns and Preferences for Charging Infrastructure for Battery Electric Vehicles in Commercial Fleets in the Hamburg Metropolitan Region Christian BLUSCH, Heike FLÄMIG and Sören Christian TRÜMPER 14.1 Introduction 14.2 State of the art/context of study 14.3 Research goal and approach 229 229 230 231 Contents 14.4 Method of data collection 14.5 Results and discussion 14.6 Conclusions 14.7 Acknowledgements 14.8 Bibliography Chapter 15 The Potential of Light Electric Vehicles for Specific Freight Flows: Insights from the Netherlands Susanne BALM, Ewoud MOOLENBURGH, Nilesh ANAND and Walther PLOOS VAN AMSTEL 15.1 Introduction 15.2 Definition of LEFV 15.3 State of the art 15.4 Methodology 15.5 Potential of LEFV for different freight flows 15.5.1 Selection of freight flows 15.5.2 Description of freight flows 15.5.3 Receivers’ perspective 15.6 Multi-criteria evaluation 15.6.1 Setup 15.6.2 Outcome 15.7 Discussion 15.8 Conclusion 15.9 Acknowledgements 15.10 Bibliography Chapter 16 Use of CNG for Urban Freight Transport: Comparisons Between France and Brazil Leise Kelli DE OLIVEIRA and Diana DIZIAIN 16.1 Introduction 16.2 Brief literature review 16.3 Methodology 16.4 Brazilian case 16.5 French case 16.6 Comparison of Brazilian and French experience 16.7 Conclusion 16.8 Acknowledgements 16.9 Bibliography xi 232 232 237 238 238 241 241 243 244 246 247 247 248 253 253 253 254 256 257 258 259 261 261 263 264 264 265 267 268 268 268 List of Authors Stanisław IWAN Maritime University of Szczecin Poland Mariusz JEDLIŃSKI Maritime University of Szczecin Poland Johan W JOUBERT University of Pretoria Hatfield South Africa Kinga KIJEWSKA Maritime University of Szczecin Poland Kyosuke KONDA Kyoto University Japan Izabela KOTOWSKA Maritime University of Szczecin Poland José Machado LEON University of Washington Seattle USA Tomislav LETNIK University of Maribor Slovenia Orlando Fontes LIMA JR LALT Unicamp Campinas Brazil 371 Fangping LU Singapore University of Technology and Design Singapore Vittorio MARZANO University of Naples Federico II Italy Esteban E MASCARINO Massachusetts Institute of Technology Cambridge USA Rahma MAULIDA Gadjah Mada University Yogyakarta Indonesia Ed MCCORMACK University of Washington Seattle USA Matej MENCINGER University of Maribor Slovenia Daniel MERCHÁN Massachusetts Institute of Technology Cambridge USA Ewoud MOOLENBURGH Amsterdam University of Applied Sciences The Netherlands 372 City Logistics Anne MOUDON University of Washington Seattle USA Bertha Maya SOPHA Gadjah Mada University Yogyakarta Indonesia Kanyarat NIMTRAKOOL University of Le Havre France Monique STINSON Massachusetts Institute of Technology Cambridge USA Hanif Arkan NURDIANSYAH Gadjah Mada University Yogyakarta Indonesia Danièle PATIER Laboratoire Aménagement Economie Transports Lyon France Walther PLOOS VAN AMSTEL Amsterdam University of Applied Sciences The Netherlands Ali Gul QURESHI Kyoto University Japan Jason SCULLY University of Washington Seattle USA André SNOECK Massachusetts Institute of Technology Cambridge USA Eiichi TANIGUCHI Kyoto University Japan Lorant TAVASSZY Delft University of Technology The Netherlands Russell G THOMPSON The University of Melbourne Australia Florence TOILIER Laboratoire Aménagement Economie Transports Lyon France Sören Christian TRÜMPER Hamburg University of Technology Germany J.H.R VAN DUIN Delft University of Technology and Delft & Rotterdam University of Applied Sciences The Netherlands List of Authors 373 Matthias WINKENBACH Massachusetts Institute of Technology Cambridge USA Fang ZHAO Singapore-MIT Alliance for Research and Technology Singapore Hugo T.Y YOSHIZAKI University of São Paulo Brazil Thomas ZUNDER Newcastle University UK Giovanni ZENEZINI Politecnico di Torino Italy Index A, B, C adaptive dynamic programming, 211–217, 220–226 adoption, 313, 317, 318, 320, 326–328 agent-based modeling, 179, 181, 187, 190, 193 algorithm, 23, 29–31, 33, 34 barriers, 313–315, 318–320, 326–328 battery electric vehicles, 229, 231, 234, 235, 237 benefit, 271–273, 275, 276, 278, 279, 281, 283–287 Brazil, 333, 334, 336, 340, 341 business model, 39–45, 48, 50–56 cargo bike, 241, 243–245, 251, 252 case study, 313, 315, 318, 320, 327, 330, 332 CBA, 272, 273, 275–278, 280, 281, 284–286 charging infrastructure, 229, 230, 232–237 cluster analysis, 132, 133, 135, 137, 138, 140 CNG, 261–268 commercial fleets, 229, 231 conceptual model, 39 consolidation, 271–273, 279, 281, 283, 287 coordination, 272, 279, 280, 282–284, 286, 287 cost, 271–276, 280–283, 285–287 D, E, F decision support system, 179, 181–188, 190, 191, 193 development, 292, 293, 305, 307 disaster waste collection, 97, 99, 113 e-commerce, 351, 352 economic model, environment, 272, 274, 277, 278, 283, 286 evaluation, 165, 170, 171, 173, 174 experiment, 2, 3, 8, 15, 17 freight data collection, 117 village, 333, 334, 336, 338, 339, 341, 344–347 fuel, 261–266, 268 City Logistics 2: Modeling and Planning Initiatives, First Edition Edited by Eiichi Taniguchi and Russell G Thompson © ISTE Ltd 2018 Published by ISTE Ltd and John Wiley & Sons, Inc 376 City Logistics G, I, L genetic algorithm, 104, 113 greedy algorithm, 104–106, 113 information system, 23, 25–28, 34 inter-organizational innovation, 314, 318, 320 last-mile delivery network, 60, 72, 73, 75 distribution, 79, 87, 88, 91 light electric vehicles, 241 loading bays, 23–33 logistics sprawl, 333–335, 340, 342–347 M, O, P measure, 1, 2, 4, 7–9, 15, 16, 19 mixed integer linear programming, 60, 75 multi-agent model, 165, 166, 168, 175 system, 211–217, 220, 226 multi-echelon location routing, 60 off-hours deliveries, 79–82, 87, 88, 91–94 policy measures, 165, 166, 168, 169, 171, 173–176 R, S, T regulatory, 1, reinforcement learning, 165–167 Saint-Etienne, 313, 315, 318–328 SCBA, 291–293, 295, 305 shipment tracking, 121, 122, 125 simulated annealing, 104, 106, 113 smartphone, 23, 27, 29, 34 stochastic programming, 61, 68 sustainability, 145, 154 transport, 143–145, 150, 152, 153 policies, 229 truck load/unload zones, 351, 355–359, 366, 368 parking, 351–353, 366 typology, 143–160 U, V, Y urban consolidation center, 2, 3, 211–213, 215, 217, 220, 222, 223, 226, 313–328 distribution center, 179–185, 187, 188, 190–193 freight, 143–146, 148–153, 155–157, 272–275, 280–283, 351–356, 359, 366 transport, 261–264, 267 goods movement, 129 logistics, 131–133, 136, 139, 140 space, 1–4, 15, 16, 18–20 vehicle survey, 121, 125, 126 Yogyakarta, 179–181, 183, 184, 187, 191–193 Other titles from in Systems and Industrial Engineering – Robotics 2018 BERRAH Lamia, CLIVILLÉ Vincent, FOULLOY Laurent Industrial Objectives and Industrial Performance: Concepts and Fuzzy Handling GONZALEZ-FELIU Jesus Sustainable Urban Logistics: Planning and Evaluation GROUS Ammar Applied Mechanical Design LEROY Alain Production Availability and Reliability: Use in the Oil and Gas Industry MARÉ Jean-Charles Aerospace Actuators 3: European Commercial Aircraft and 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Scheduling THIERRY Caroline, THOMAS André, BEL Gérard Supply Chain Simulation and Management 2007 DE LARMINAT Philippe Analysis and Control of Linear Systems DOMBRE Etienne, KHALIL Wisama Robot Manipulators LAMNABHI Franỗoise et al Taming Heterogeneity and Complexity of Embedded Control LIMNIOS Nikolaos Fault Trees 2006 FRENCH COLLEGE OF METROLOGY Metrology in Industry NAJIM Kaddour Control of Continuous Linear Systems ... xi 23 2 23 2 23 7 23 8 23 8 24 1 24 1 24 3 24 4 24 6 24 7 24 7 24 8 25 3 25 3 25 3 25 4 25 6 25 7 25 8 25 9 26 1 26 1 26 3 26 4 26 4 26 5 26 7 26 8 26 8 26 8 xii City Logistics Chapter 17 Using Cost–Benefit... 21 1 21 2 21 2 21 3 21 4 21 5 21 7 21 8 22 0 22 1 22 2 22 3 22 6 22 6 Chapter 14 Use Patterns and. .. 320 320 320 321 322 322 323 324 326 328 328 Chapter 20 Logistics Sprawl Assessment Applied to Locational Planning: A Case Study in