Title Details

Russell G. Thompson, The University of Melbourne, Australia.

Preface xv

Chapter 1. Urban Logistics Spaces: What Models, What Uses and What Role for Public Authorities? 1
Danièle PATIER and Florence TOILIER

1.1. Introduction 1

1.2. Literature review 2

1.3. ULS typology  . 4

1.3.1. The Urban Logistics Zone (ULZ) or freight village 4

1.3.2. The Urban Distribution Center (UDC) 6

1.3.3. Vehicle Reception Points (VRP) 9

1.3.4. Goods Reception Points (GRP) 12

1.3.5. The Urban Logistics Box (ULB) 13

1.3.6. Mobile Urban Logistics Spaces (mULS) 15

1.4. Recommendations 18

1.5. Conclusion 19

1.6. Bibliography 20

Chapter 2. Dynamic Management of Urban Last-Mile Deliveries 23
Tomislav LETNIK, Matej MENCINGER and Stane BOZICNIK

2.1. Introduction 23

2.2. Review of urban freight loading bay problems and solutions 25

2.3. Information system for dynamic management of urban last-mile deliveries 26

2.4. Algorithm for dynamic management of urban freight deliveries 29

2.5. Application of the model to a real case 32

2.6. Conclusions 33

2.7. Bibliography 34

Chapter 3. Stakeholders’ Roles for Business Modeling in a City Logistics Ecosystem: Towards a Conceptual Model 39
Giovanni ZENEZINI, J.H.R. VAN DUIN, Lorant TAVASSZY and Alberto DE MARCO

3.1. Introduction 39

3.2. Research background 41

3.2.1. Business model concept 41

3.2.2. Business ecosystem 42

3.2.3. Role-based networks and ecosystems 43

3.3. The CL business model framework: roles, business entities and value exchanges 43

3.4. City logistics concepts and role assignment 48

3.4.1. Parcel lockers installation: MyPUP 48

3.4.2. Urban consolidation centers 51

3.4.3. Business model implications 54

3.5. Conclusions 55

3.6. Bibliography 56

Chapter 4. Establishing a Robust Urban Logistics Network at FEMSA through Stochastic Multi-Echelon Location Routing 59
André SNOECK, Matthias WINKENBACH and Esteban E. MASCARINO

4.1. Introduction 59

4.2. Strategic distribution network design 62

4.2.1. Distribution network 63

4.2.2. Network cost 63

4.2.3. Distribution cost 64

4.2.4. Optimization model 65

4.3. Solution scheme 67

4.3.1. Scenario generation and selection 67

4.3.2. Design generation 68

4.3.3. Design evaluation 68

4.4. Case study 68

4.4.1. Data and parameters 69

4.4.2. Analysis results 70

4.5. Results 71

4.5.1. Design generation 71

4.5.2. Design evaluation 72

4.5.3. Sensitivity to cost of lost sales 73

4.6. Conclusion 75

4.7. Bibliography 75

Chapter 5. An Evaluation Model of Operational and Cost Impacts of Off-Hours Deliveries in the City of São Paulo, Brazil 79
Cláudio B. CUNHA and Hugo T.Y. YOSHIZAKI

5.1. Introduction 79

5.2. Literature review 81

5.3. Proposed approach 84

5.4. Scenario generation 87

5.5. Results 90

5.6. Concluding remarks 94

5.7. Bibliography 94

Chapter 6. Application of the Bi-Level Location-Routing Problem for Post-Disaster Waste Collection 97
Cheng CHENG, Russell G. THOMPSON, Alysson M. COSTA and Xiang HUANG

6.1. Introduction 97

6.2. Model formulation 99

6.3. Solution algorithm 104

6.3.1. Genetic Algorithms 104

6.3.2. Greedy Algorithm 105

6.3.3. Simulated Annealing 106

6.4. Case study 106

6.4.1. Case study area 106

6.5. Result analysis 109

6.5.1. Models comparison 109

6.5.2. Sensitivity analysis 111

6.6. Conclusion 113

6.7. Bibliography 114

Chapter 7. Next-Generation Commodity Flow Survey: A Pilot in Singapore 117
Lynette CHEAH, Fang ZHAO, Monique STINSON, Fangping LU, Jing DING-MASTERA, Vittorio MARZANO, and Moshe BEN-AKIVA

7.1. Introduction 117

7.2. Integrated commodity flow survey 119

7.2.1. Overview 119

7.3. Key survey features 121

7.3.1. Sampling related supply network entities 121

7.3.2. Multiple survey instruments leveraging sensing technologies 121

7.3.3. A unified web-based survey platform 122

7.4. Pilot survey implementation 123

7.4.1. Sample design and recruitment 124

7.4.2. Shipment and vehicle tracking methods 125

7.4.3. Pilot survey experience and lessons learnt 126

7.4.4. Preliminary data analysis 127

7.5. Conclusion 129

7.6. Acknowledgements 129

7.7. Bibliography 130

Chapter 8. City Logistics and Clustering: Impacts of Using HDI and Taxes 131
Rodrigo Barros CASTRO, Daniel MERCH N, Orlando Fontes LIMA JR and Matthias WINKENBACH

8.1. Introduction 131

8.2. Methodology 133

8.2.1. Principal component analysis 135

8.2.2. K-means clustering 135

8.3. Results 135

8.4. Conclusion 140

8.5. Bibliography 140

Chapter 9. Developing a Multi-Dimensional Poly-Parametric Typology for City Logistics 143
Paulus ADITJANDRA and Thomas ZUNDER

9.1. Introduction 143

9.2. Literature review 144

9.3. Methodology 145

9.4. Evaluation and analysis 146

9.4.1. Inventory of all EU projects 146

9.4.2. Inventory of typologies 147

9.4.3. Land use typologies 148

9.4.4. Measure typologies 149

9.4.5. Urban freight markets 151

9.4.6. Traffic flow typology 152

9.4.7. Impacts 153

9.4.8. Gaps 153

9.5. Validation and enhancement of the inventory 154

9.6. Proposed typology 155

9.6.1. Approach 155

9.6.2. Dimension: Why? 157

9.6.3. Dimension: Where? 157

9.6.4. Dimension: Who? 158

9.6.5. Dimension: What? 158

9.6.6. Dimension: How? 159

9.7. Reflections 159

9.8. Conclusion 160

9.9. Acknowledgements 160

9.10. Bibliography 160

Chapter 10. Multi-agent Simulation with Reinforcement Learning for Evaluating a Combination of City Logistics Policy Measures 165
Eiichi TANIGUCHI, Ali Gul QURESHI and Kyosuke KONDA

10.1. Introduction 165

10.2. Literature review 166

10.3. Models 166

10.4. Case studies in Osaka and Motomachi 168

10.4.1. Settings 168

10.4.2. Results 170

10.5. Conclusion 175

10.6. Bibliography 176

Chapter 11. Decision Support System for an Urban Distribution Center Using Agent-based Modeling: A Case Study of Yogyakarta Special Region Province, Indonesia 179
Bertha Maya SOPHA, Anna Maria Sri ASIH, Hanif Arkan NURDIANSYAH and Rahma MAULIDA

11.1. Introduction 179

11.2. Theoretical background 182

11.2.1. Urban distribution center 182

11.2.2. Decision support system of city logistics 183

11.3. The proposed decision support system 184

11.3.1. System characterization 184

11.3.2. The logical architecture 185

11.3.3. Agent-based modeling (ABM) 187

11.3.4. Model verification and validation 190

11.4. Example of application: the case of Yogyakarta Special Region 191

11.5. Conclusion 192

11.6. Acknowledgements 193

11.7. Bibliography 194

Chapter 12. Evaluating the Relocation of an Urban Container Terminal 197
Johan W. JOUBERT

12.1. Introduction 197

12.2. Methodology 199

12.2.1. MATSim 199

12.2.2. Initial demand 200

12.2.3. Alternative scenarios 201

12.3. Results 201

12.3.1. Directly affected vehicles 202

12.3.2. Extended effects 205

12.4. Conclusion 208

12.5. Acknowledgements 209

12.6. Bibliography 209

Chapter 13. Multi-Agent Simulation Using Adaptive Dynamic Programing for Evaluating Urban Consolidation Centers 211
Nailah FIRDAUSIYAH, Eiichi TANIGUCHI and Ali Gul QURESHI

13.1. Introduction 211

13.2. Literature review 212

13.2.1. Evaluation models for city logistics measures 212

13.2.2. ADP for evaluating city logistics measures 213

13.3. Models 214

13.3.1. Freight carrier’s MAS-ADP model 215

13.3.2. Freight carrier’s MAS Q-learning model 217

13.3.3. Vehicle routing problem with soft time windows (VRPSSTW) 218

13.4. Case study 220

13.5. Results and discussions 221

13.5.1. Case 0 (base case) 222

13.5.2. Case 1 223

13.6. Conclusion and future work 226

13.7. Bibliography 226

Chapter 14. Use Patterns and Preferences for Charging Infrastructure for Battery Electric Vehicles in Commercial Fleets in the Hamburg Metropolitan Region 229
Christian BLUSCH, Heike FLÄMIG and Sören Christian TRÜMPER

14.1. Introduction 229

14.2. State of the art/context of study 230

14.3. Research goal and approach 231

14.4. Method of data collection 232

14.5. Results and discussion 232

14.6. Conclusions 237

14.7. Acknowledgements 238

14.8. Bibliography 238

Chapter 15. The Potential of Light Electric Vehicles for Specific Freight Flows: Insights from the Netherlands 241
Susanne BALM, Ewoud MOOLENBURGH, Nilesh ANAND and

Walther PLOOS VAN AMSTEL

15.1. Introduction 241

15.2. Definition of LEFV 243

15.3. State of the art 244

15.4. Methodology 246

15.5. Potential of LEFV for different freight flows 247

15.5.1. Selection of freight flows 247

15.5.2. Description of freight flows 248

15.5.3. Receivers’ perspective 253

15.6. Multi-criteria evaluation 253

15.6.1. Setup 253

15.6.2. Outcome 254

15.7. Discussion 256

15.8. Conclusion 257

15.9. Acknowledgements 258

15.10. Bibliography 259

Chapter 16. Use of CNG for Urban Freight Transport: Comparisons Between France and Brazil 261
Leise Kelli DE OLIVEIRA and Diana DIZIAIN

16.1. Introduction 261

16.2. Brief literature review 263

16.3. Methodology 264

16.4. Brazilian case 264

16.5. French case 265

16.6. Comparison of Brazilian and French experience 267

16.7. Conclusion 268

16.8. Acknowledgements 268

16.9. Bibliography 268

Chapter 17. Using Cost–Benefit Analysis to Evaluate City Logistics Initiatives: An Application to Freight Consolidation in Small- and Mid-Sized Urban Areas 271
Johan HOLMGREN

17.1. Introduction 271

17.2. Characteristics of city logistics and some terminology 273

17.2.1. Efficiency in city logistics 274

17.2.2. Evaluation methods 275

17.3. Potential costs and benefits of implementing urban consolidation centers 279

17.4. Coordinated freight distribution in Linköping 280

17.5. Evaluating urban freight initiatives by cost–benefit analysis 281

17.6. The problem of cost allocation 286

17.7. Conclusion 286

17.8. Bibliography 287

Chapter 18. Assumptions of Social Cost–Benefit Analysis for Implementing Urban Freight Transport Measures 291
Izabela KOTOWSKA, Stanisław IWAN, Kinga KIJEWSKA and Mariusz JEDLIŃSKI

18.1. Introduction 291

18.2. The assumptions for utilization of SCBA in city logistics 295

18.2.1. External air pollution cost 296

18.2.2. Marginal climate change costs 299

18.2.3. Marginal accident costs 301

18.2.4. Congestion costs 302

18.2.5. Marginal external noise costs 304

18.2.6. Employment growth and development of local economy 305

18.2.7. Final calculations 308

18.3. Conclusions 310

18.4. Acknowledgements 310

18.5. Bibliography 310

Chapter 19. Barriers to the Adoption of an Urban Logistics Collaboration Process: A Case Study of the Saint-Etienne Urban Consolidation Centre 313
Kanyarat NIMTRAKOOL, Jesus GONZALEZ-FELIU and Claire CAPO

19.1. Introduction 313

19.2. Background and theoretical framework 315

19.2.1. The stakeholders in an urban logistics collaboration project 315

19.2.2. Urban Consolidation Centre (UCC) as an organizational innovation 316

19.2.3. Barriers in urban logistics projects 318

19.3. Research methodology 320

19.3.1. The research approach 320

19.3.2. Qualitative study: selection of respondents 320

19.3.3. Quantitative analysis: purpose and CBA methodology 321

19.4. Results 322

19.4.1. The UCC of Saint-Etienne: background and objectives 322

19.4.2. Operation aspects 323

19.4.3. The conditions of economic viability of Saint-Etienne’s UCC 324

19.4.4. Barriers identified by stakeholders 326

19.5. Conclusions 328

19.6. Bibliography 328

Chapter 20. Logistics Sprawl Assessment Applied to Locational Planning: A Case Study in Palmas (Brazil) 333
Lilian dos Santos Fontes Pereira BRACARENSE, Thiago Alvares ASSIS, Leise Kelli DE OLIVEIRA and Renata Lúcia Magalhães DE OLIVEIRA

20.1. Introduction 333

20.2. Logistics sprawl and the importance of logistics facilities’ location 334

20.3. Methodology 335

20.4. Area of study 339

20.4.1. Logistics sprawl assessment and scenario comparison 342

20.5. Conclusion 347

20.6. Acknowledgements 348

20.7. Bibliography 348

Chapter 21. Are Cities’ Delivery Spaces in the Right Places? Mapping Truck Load/Unload Locations 351
Anne GOODCHILD, Barb IVANOV, Ed MCCORMACK, Anne MOUDON, Jason SCULLY, José Machado LEON and Gabriela GIRON VALDERRAMA

21.1. Introduction 351

21.2. Moving more goods, more quickly 352

21.3. Establishment of a well-defined partnership 353

21.4. The Final 50 Feet project 354

21.5. Getting granular 356

21.6. Mapping the city’s freight delivery infrastructure 358

21.6.1. Step 1: collect existent data 358

21.6.2. Step 2: develop survey to collect freight bay and loading dock data 358

21.6.3. Preliminary site visits 359

21.6.4. Initial survey form and the pilot survey 360

21.6.5. Step 3: implement the survey 363

21.7. Research results 366

21.8. Conclusion 368

21.9. Bibliography 368

List of Authors 369

Index 375