Robust Repositioning for Vehicle Sharing

Forthcoming in Manufacturing & Service Operations Management

40 Pages Posted: 26 May 2017 Last revised: 27 May 2018

See all articles by Long He

Long He

George Washington University

Zhenyu Hu

National University of Singapore (NUS)

Meilin Zhang

National University of Singapore (NUS) - NUS Business School

Date Written: March 31, 2018

Abstract

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 multi-region 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 both as an illustration of the DRO framework as well as a benchmark for the later multi-stage model. We then develop a computationally efficient multi-stage DRO model with enhanced linear decision rule (ELDR).

Results: Under a 2-region system, we find that a simple reposition up-to and down-to policy to be optimal, when the demands are temporally independent. Such structure is also preserved by our ELDR solution. We also provide new analytical insights by proving the optimality of ELDR in solving single-period DRO problem. We then show that the numerical performance of 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.

Keywords: Fleet Repositioning; Vehicle Sharing; Dynamic Program; Robust Optimization

Suggested Citation

He, Long and Hu, Zhenyu and Zhang, Meilin, Robust Repositioning for Vehicle Sharing (March 31, 2018). Forthcoming in Manufacturing & Service Operations Management, Available at SSRN: https://ssrn.com/abstract=2973739 or http://dx.doi.org/10.2139/ssrn.2973739

Long He (Contact Author)

George Washington University ( email )

2121 I Street NW
Washington, DC 20052
United States

Zhenyu Hu

National University of Singapore (NUS) ( email )

1E Kent Ridge Road
NUHS Tower Block Level 7
Singapore, 119228
Singapore

Meilin Zhang

National University of Singapore (NUS) - NUS Business School ( email )

1 Business Link
Singapore, 117592
Singapore

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
839
Abstract Views
2,785
Rank
53,370
PlumX Metrics