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Battery swapping station location routing problem: A Cooperative Business Model  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:Battery swapping station location routing problem: A Cooperative Business Model

作者:Li, Ying[1];Li, Feifan[1];Li, Qiuyi[1];Zhang, Pengwei[1]

机构:[1]East China Univ Sci & Technol, Business Sch, 130,Meilong Rd, Shanghai 200237, Peoples R China

年份:2025

卷号:200

外文期刊名:COMPUTERS & INDUSTRIAL ENGINEERING

收录:;EI(收录号:20245117530215);WOS:【SCI-EXPANDED(收录号:WOS:001385740100001)】;

基金:This work was supported by the National Natural Science Foundation of China (No. 72074076) .

语种:英文

外文关键词:Battery swapping station; Battery electric vehicles; Cooperative Business Model; Location-routing problem

摘要:In the context of green logistics, the promotion of electric logistics vehicles is gaining momentum, but the process is constrained by insufficient infrastructure, such as battery swapping stations (BSSs). The high costs of BSS make it challenging for companies to expand their construction, while the low penetration rate of EVs further diminishes the investment value and motivation, creating a circular dependency problem. To address this challenge, cooperative models can be employed to lower investment barriers through cost sharing and encourage broader enterprise participation in constructing BSSs. This study focuses on the battery swapping station location-routing problem (BSS-LRP) by introducing a "Cooperative Business Model" in which logistics companies can complete or participate in BSSs construction based on their operational needs. The model considers battery electric vehicles (BEVs) load, range, etc., and aims to minimize the investment, usage, and BEVs transport costs of BSSs. A heuristic algorithm, Simulated Annealing-K-Means-Ant Colony Optimization (SA-K-Means-ACO), is designed to solve this problem. The effectiveness and accuracy of the proposed algorithm have been validated by comparing it with the TS-ACO algorithm, especially when dealing with clustered customer instances. Furthermore, research conducted a comprehensive experiment to discuss the impact of battery capacity, Investment, and usage costs, offering insights for logistics companies.

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