Title: Recharge strategies for the electric vehicle routing problem with soft time windows and fast chargers

Authors: Teng Ren; Shuxuan Li; Yongming He; Chenglin Xiao; Ke Zhang; Guohua Wu; Zhenping Li

Addresses: School of Logistics and Transportation, Central South University of Forestry and Technology, Changsha, China ' School of Logistics and Transportation, Central South University of Forestry and Technology, Changsha, China ' Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G-2V4, Canada ' School of Logistics and Transportation, Central South University of Forestry and Technology, Changsha, China ' School of Business, Central South University, Changsha, China ' School of Traffic and Transportation Engineering, Central South University, Changsha, China ' School of Information, Beijing Wuzi University, Beijing, China

Abstract: At present, under the pressure of environmental pollution, logistics enterprises are beginning to use electric vehicles for various distribution services due to their low energy consumption and environmentally friendly nature. Considering the fact that the quality of service and charging strategy have an important impact on the electric vehicle routing problem, to improve the efficiency of electric vehicles in logistics distribution networks, we investigate an electric vehicle routing problem with soft time windows and fast charging (EVRPSTW-FC) stations: mixed integer linear programming is established to minimise total logistics energy consumption. To solve the proposed model, a hybrid adaptive genetic algorithm (HAGA) is proposed. The performance of HAGA is compared and tested with benchmark examples, and the results verify the feasibility and effectiveness of the proposed model and solution algorithm.

Keywords: electric vehicle; hybrid adaptive genetic algorithm; HAGA; vehicle routing problem; soft time windows.

DOI: 10.1504/IJAAC.2021.116420

International Journal of Automation and Control, 2021 Vol.15 No.4/5, pp.444 - 462

Received: 18 Jul 2019
Accepted: 01 Oct 2019
Published online: 23 Jul 2021 *

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