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Branch-and-price-and-cut for the electric vehicle relocation problem in one-way carsharing systems

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  • Qin, Hu
  • Su, E.
  • Wang, Yilun
  • Li, Jiliu

Abstract

We study the electric vehicle (EV) relocation problem that arises in the practice of one-way carsharing systems. In this problem, users are allowed to pick up an EV from one parking station and return it to another station. To maintain a spatially balanced distribution of vehicles, the system creates a set of pickup and delivery requests of EVs, which are to be served by a team of workers. The objective of this problem is to find a set of routes for workers to fulfill EV pickup and delivery requests, such that the total operational profit is maximized. The battery level of EV and the working duration limit for workers make the problem complicated and challenging. We formulate a set-packing model for this problem, and design a branch-and-cut-and-price algorithm to solve it. Particularly, we design a bi-directional label-setting algorithm to deal with the pricing subproblem, and implement two acceleration techniques, namely, decremental space relaxation and heuristic column generator. We also consider another objective of minimizing the total route duration for this problem, and the proposed algorithm still works with some adaptations applied. On a set of randomly generated instances, we conduct extensive computational experiments, and the results demonstrate the effectiveness of the proposed algorithm. Besides, the impacts of instance parameters and the efficiency of acceleration techniques have been analyzed.

Suggested Citation

  • Qin, Hu & Su, E. & Wang, Yilun & Li, Jiliu, 2022. "Branch-and-price-and-cut for the electric vehicle relocation problem in one-way carsharing systems," Omega, Elsevier, vol. 109(C).
    • Handle: RePEc:eee:jomega:v:109:y:2022:i:c:s0305048322000184
    DOI: 10.1016/j.omega.2022.102609
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    2. Bitencourt, Leonardo & Dias, Bruno & Soares, Tiago & Borba, Bruno & Quirós-Tortós, Jairo, 2023. "e-Carsharing siting and sizing DLMP-based under demand uncertainty," Applied Energy, Elsevier, vol. 330(PB).

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