© 2023 Elsevier LtdAs an emerging and sustainable technology, electric vehicles (EVs) are becoming increasingly popular in the transportation system. However, they still have limitations in terms of energy capacity and high consumption. By implementing flexible scheduling for EV routings, it is hoped that improved efficiency and saved energy consumption can be achieved. This paper focuses on studying a time-dependent electric vehicle routing and scheduling problem with time windows (TDEVRSPTW). The goal is to minimize the total cost of energy consumption, travel distance, and the number of EVs, while considering vehicle travel process scheduling that allows stops at any nodes and along any arcs during periods of time-dependent congestion. For the first time, a mixed integer linear programming model is formulated for the problem, allowing for optimal solutions to small-scale problems using CPLEX. Meanwhile, a variable neighborhood search with partial model (VNS-PM) method is developed to handle the large-scale problem with 200 customers and obtain effective solutions. The numerical experiments demonstrate significant savings in energy consumption by the vehicle travel process scheduling. The proposed method further validates its strong performance by finding 11 new best solutions out of the 56 related EVRPTW benchmark instances. In addition, a case study is conducted to verify the application and energy consumption savings of the proposed problem, which also derives some additional recommendations by sensitivity analysis.
https://doi.org/10.1016/j.jclepro.2023.140188Cite as:
@article{Zhou_2024, title={A MILP model and heuristic method for the time-dependent electric vehicle routing and scheduling problem with time windows}, volume={434}, ISSN={0959-6526}, url={http://dx.doi.org/10.1016/j.jclepro.2023.140188}, DOI={10.1016/j.jclepro.2023.140188}, journal={Journal of Cleaner Production}, publisher={Elsevier BV}, author={Zhou, Saiqi and Zhang, Dezhi and Ji, Bin and Zhou, Shaoyu and Li, Shuangyan and Zhou, Likun}, year={2024}, month=jan, pages={140188} }