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An electric vehicle routing model with charging stations consideration for sustainable logistics

Yan Li (Chongqing Changan Minsheng APLL Logistics Co., Ltd, Chongqing, China) (School of Management Science and Real Estate, Chongqing University, Chongqing, China)
Ming K. Lim (Adam Smith Business School, University of Glasgow, Glasgow, UK)
Weiqing Xiong (College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China)
Xingjun Huang (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China)
Yuhe Shi (School of Management Science and Real Estate, Chongqing University, Chongqing, China)
Songyi Wang (Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China) (Peng Cheng Laboratory, Shenzhen, China)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 26 December 2023

Issue publication date: 16 February 2024

353

Abstract

Purpose

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

Design/methodology/approach

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

Findings

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

Research limitations/implications

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

Originality/value

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

Keywords

Acknowledgements

This research is funded by Natural Science Foundation of Chongqing, China (No: CSTB2023NSCQ-BHX0084).

Citation

Li, Y., Lim, M.K., Xiong, W., Huang, X., Shi, Y. and Wang, S. (2024), "An electric vehicle routing model with charging stations consideration for sustainable logistics", Industrial Management & Data Systems, Vol. 124 No. 3, pp. 1076-1106. https://doi.org/10.1108/IMDS-08-2023-0581

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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