Hybrid electric vehicle routing problem with mode selection. Issue 2 (17th January 2020)

• Record Type: Journal Article
• Authors: Zhen, Lu; Xu, Ziheng; Ma, Chengle; Xiao, Liyang
• Is Part Of: International journal of production research. Volume 58:Issue 2(2020)
• Page Start: 562
• Page End: 576
• Publication Date: 2020-01-17
• Subjects: Vehicle routing -- hybrid vehicle -- mode selection -- particle swarm optimization -- mixed integer linear programming; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2019.1598593
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : With the development of green logistics, logistics companies gradually are paying attention to the application of hybrid electric vehicles (HEVs). HEVs have the advantages of low energy consumption and pollution, while their disadvantage mainly lies in their limited continuous driving range. Therefore, it is necessary to optimize the use of fuel during the distribution process. We study the mode selection system in HEVs based on the background of green logistics and the above characteristics of HEVs. The mode selection system can adjust the driving mode of the HEV according to different road conditions to obtain the optimal use of fuel. In this paper, we propose a new study of a hybrid electric vehicle routing problem with mode selection. This problem is formulated as a mixed integer linear programming model. An improved particle swarm optimization algorithm (IPSO) is developed to solve this problem. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method. The experimental results show that our proposed algorithm not only obtains the optimal solution for some small-scale problem instances and some medium-scale problems but also solves some large-scale situations (one hundred customers, eleven vehicles, eleven charging stations, eleven gas stations and four modes) within an hour.