The impact of depot location,fleet composition and routing on emissions in city logistics |
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| Institution: | 1. CIRRELT, Canada Research Chair in Distribution Management and HEC Montréal,\nMontréal H3T 2A7, Canada;2. Southampton Business School and Centre for Operational Research, Management Science and Information Systems (CORMSIS),\nUniversity of Southampton, Southampton SO17 1BJ, United Kingdom;3. CIRRELT and HEC Montréal, Montréal H3T 2A7, Canada;1. Institute for Transport Studies, University Road, University of Leeds, Leeds LS2 9JT, United Kingdom;2. Mott MacDonald, 4th Floor, 9 Portland Street, Manchester M1 3BE, United Kingdom;1. Canada Research Chair in Distribution Management, HEC Montréal, Montréal H3T 2A7, Canada;2. Panalpina Centre for Manufacturing and Logistics Research, Cardiff Business School, Cardiff University, Cardiff CF10 3EU, UK;3. Naveen Jindal School of Management, University of Texas at Dallas, Richardson, TX 75080-3021, USA;4. School of Industrial Engineering, Eindhoven University of Technology, Eindhoven 5600MB, The Netherlands;1. Department of Combinatorics and Optimization, Faculty of Mathematics, University of Waterloo, Canada;2. Department of Industrial and Systems Engineering, University of Minnesota, United States;3. Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, United States |
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| Abstract: | This paper investigates the combined impact of depot location, fleet composition and routing decisions on vehicle emissions in city logistics. We consider a city in which goods need to be delivered from a depot to customers located in nested zones characterized by different speed limits. The objective is to minimize the total depot, vehicle and routing cost, where the latter can be defined with respect to the cost of fuel consumption and CO2 emissions. A new powerful adaptive large neighborhood search metaheuristic is developed and successfully applied to a large pool of new benchmark instances. Extensive analyses are performed to empirically assess the effect of various problem parameters, such as depot cost and location, customer distribution and heterogeneous vehicles on key performance indicators, including fuel consumption, emissions and operational costs. Several managerial insights are presented. |
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