The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion |
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| Institution: | 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. Università degli Studi di Modena e Reggio Emilia, Dipartimento di Scienze e Metodi dell’Ingegneria, Italy;2. Universidade Federal do Rio de Janeiro, Departamento de Engenharia de Sistemas e Computação, Brazil;3. Universidade Federal da Paraíba, Departamento de Engenharia de Produção, Brazil;4. Pontifícia Universidade Católica do Rio de Janeiro, Departamento de Informática, Brazil;1. Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China;2. School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, Netherlands;1. School of Reliability and System Engineering, Beihang University, Beijing 100191, China;2. Information Sciences and Technology, Penn State Berks, Tulpehocken Road, P.O. Box 7009, Reading, PA 19610-6009, United States;3. School of Engineering & Applied Science, Aston University, Birmingham B4 7ET, United Kingdom |
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| Abstract: | The green vehicle routing and scheduling problem (GVRSP) aims to minimize green-house gas emissions in logistics systems through better planning of deliveries/pickups made by a fleet of vehicles. We define a new mixed integer liner programming (MIP) model which considers heterogeneous vehicles, time-varying traffic congestion, customer/vehicle time window constraints, the impact of vehicle loads on emissions, and vehicle capacity/range constraints in the GVRSP. The proposed model allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data. A hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the problem. |
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| Keywords: | Vehicle routing Vehicle scheduling Green logistics Mixed integer programming Hybrid optimization Matheuristics |
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