The fleet size and mix pollution-routing problem |
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| Institution: | 1. CORMSIS and Southampton Business School, University of Southampton, Southampton SO17 1BJ, United Kingdom;2. CIRRELT and HEC Montréal, 3000, chemin de la Côte-Sainte-Catherine, Montréal H3T 2A7, Canada;3. CIRRELT and Canada Research Chair in Distribution Management, HEC Montréal, 3000, chemin de la Côte-Sainte-Catherine, Montréal H3T 2A7, Canada;1. School of Industrial Engineering and Operations, Planning, Accounting and Control (OPAC), Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands;2. School of Management and Centre for Operational Research, Management Science and Information Systems (CORMSIS), University of Southampton, Southampton, Highfield SO17 1BJ, United Kingdom;3. Canada Research Chair in Distribution Management and Interuniversity Research Centre on Enterprise Networks, Logistics, and Transportation (CIRRELT), HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, Montréal H3T 2A7, Canada;1. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;2. Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran;3. Industrial Engineering Department, Faculty of Engineering, Shahed University, Tehran, Iran;1. School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran;2. Panalpina Centre for Manufacturing and Logistics Research, Cardiff Business School, Cardiff University, Cardiff CF10 3EU, UK |
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| Abstract: | This paper introduces the fleet size and mix pollution-routing problem which extends the pollution-routing problem by considering a heterogeneous vehicle fleet. The main objective is to minimize the sum of vehicle fixed costs and routing cost, where the latter can be defined with respect to the cost of fuel and CO2 emissions, and driver cost. Solving this problem poses several methodological challenges. To this end, we have developed a powerful metaheuristic which was successfully applied to a large pool of realistic benchmark instances. Several analyses were conducted to shed light on the trade-offs between various performance indicators, including capacity utilization, fuel and emissions and costs pertaining to vehicle acquisition, fuel consumption and drivers. The analyses also quantify the benefits of using a heterogeneous fleet over a homogeneous one. |
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| Keywords: | Vehicle routing Fuel consumption Heterogeneous fleet Evolutionary metaheuristic |
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