Robust Repositioning for Vehicle Sharing

• 成果类型: Article
• 署名作者: He, Long; Hu, Zhenyu; Zhang, Meilin
• 署名单位: National University of Singapore; Singapore University of Social Sciences (SUSS)
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2018.0734
• 发表日期: 2020
• 页码: 241-256
• 关键词: fleet repositioning; vehicle sharing; Dynamic program; robust optimization

摘要

Problem definition: In this paper, we study the fleet repositioning problem for a free-float vehicle sharing system, aiming to dynamically match the vehicle supply and travel demand at the lowest total cost of repositioning and lost sales. Academic/practical relevance: Besides the analytical results on the optimal repositioning policy, the proposed optimization framework is applicable to practical problems by its computational efficiency as well as the capability to handle temporally dependent demands. Methodology: We first formulate the problem as a stochastic dynamic program. To solve for a multiregion system, we deploy the distributionally robust optimization (DRO) approach that can incorporate demand temporal dependence, motivated by real data. We first propose a myopic two-stage DRO model that serves as both an illustration of the DRO framework and a benchmark for the later multistage model. We then develop a computationally efficient multistage DRO model with an enhanced linear decision rule (ELDR). Results: Under a two-region system, we find a simple reposition up-to and down-to policy to be optimal, when the demands are temporally independent. Such a structure is also preserved by our ELDR solution. We also provide new analytical insights by proving the optimality of ELDR in solving the single-period DRO problem. We then show that the numerical performance of the ELDR solution is close to the exact optimal solution from the dynamic program. Managerial implications: In a real-world case study of car2go, we quantify the value of repositioning and compare with several benchmarks to demonstrate that the ELDR solutions are computationally scalable and in general result in lower cost with less frequent repositioning. We also explore several managerial implications and extensions from the experiments.