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The vehicle deployment and relocation problem for electric vehicle sharing systems considering demand and parking space stochasticity

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  • Lin, Dung-Ying
  • Kuo, Jia-Kai

Abstract

One-way electric vehicle (EV) sharing systems have received increasing attention because of their environmental, mobility, and societal benefits. In this research, we investigate the vehicle deployment and relocation problem for hybrid one-way station-based and free-floating electric vehicle sharing systems considering demand and parking space stochasticity. The problem is formulated as a two-stage stochastic program. In the first stage, the EVs are deployed into a sharing system based on historical customer data to maximize the resulting profit. The second stage maximizes the expected profit by relocating the EVs in the system using relocation staff in response to an uncertain demand. To solve the resulting complicated problem, an integer L-shaped method is proposed so that the cuts can efficiently fathom the solution space and identify solutions with a reasonable optimality gap. The proposed solution framework is empirically applied to cases of various sizes. The empirical results demonstrate the effectiveness of the proposed solution scheme.

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  • Lin, Dung-Ying & Kuo, Jia-Kai, 2021. "The vehicle deployment and relocation problem for electric vehicle sharing systems considering demand and parking space stochasticity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    • Handle: RePEc:eee:transe:v:156:y:2021:i:c:s1366554521002751
    DOI: 10.1016/j.tre.2021.102514
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    4. Zhang, Si & Sun, Huijun & Liu, Yang & Lv, Ying & Wu, Jianjun & Feng, Xiaoyan, 2024. "Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    5. Liu, Yang & Xie, Jiaohong & Chen, Nan, 2022. "Stochastic one-way carsharing systems with dynamic relocation incentives through preference learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    6. Wang, Dong & Liao, Feixiong, 2023. "Incentivized user-based relocation strategies for moderating supply–demand dynamics in one-way car-sharing services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).

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