TY - JOUR
T1 - A data-driven robust optimization model for repositioning problem in bike-sharing systems
AU - Zhang, Runhao
AU - Xie, Chi
AU - Long, Daniel Zhuoyu
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/9
Y1 - 2025/9
N2 - In this paper, we study a multi-period repositioning problem in a free-floating bike-sharing system by proposing a novel data-driven robust optimization model. We first analyze a stochastic optimization model based on an empirical distribution, which we reformulate as a dynamic programming model. However, we highlight the computational challenges associated with solving this stochastic model, particularly in large-scale settings. To overcome these challenges, we propose a sample-based robust optimization (SRO) approach. This method constructs multiple uncertainty sets for demand using historical data and optimizes the solution under the worst-case scenario, ensuring robustness against demand variability. The proposed SRO approach guarantees asymptotic optimality and, through a linear decision rule approximation, can be reformulated into a computationally tractable linear programming model. Numerical experiments demonstrate the superiority of the SRO model over the traditional mean value problem (MVP) approach, across various performance criteria. Specifically, the SRO model achieves an average total cost reduction ranging from 5.9% to 24.3%. Our findings show the effectiveness of the SRO framework in addressing the complexities of bike-sharing repositioning under uncertainty.
AB - In this paper, we study a multi-period repositioning problem in a free-floating bike-sharing system by proposing a novel data-driven robust optimization model. We first analyze a stochastic optimization model based on an empirical distribution, which we reformulate as a dynamic programming model. However, we highlight the computational challenges associated with solving this stochastic model, particularly in large-scale settings. To overcome these challenges, we propose a sample-based robust optimization (SRO) approach. This method constructs multiple uncertainty sets for demand using historical data and optimizes the solution under the worst-case scenario, ensuring robustness against demand variability. The proposed SRO approach guarantees asymptotic optimality and, through a linear decision rule approximation, can be reformulated into a computationally tractable linear programming model. Numerical experiments demonstrate the superiority of the SRO model over the traditional mean value problem (MVP) approach, across various performance criteria. Specifically, the SRO model achieves an average total cost reduction ranging from 5.9% to 24.3%. Our findings show the effectiveness of the SRO framework in addressing the complexities of bike-sharing repositioning under uncertainty.
KW - Bike-sharing system
KW - Fleet repositioning
KW - Multi-stage decision programming
KW - Sample-based robust optimization
UR - https://www.scopus.com/pages/publications/105007061042
U2 - 10.1016/j.tre.2025.104192
DO - 10.1016/j.tre.2025.104192
M3 - Article
AN - SCOPUS:105007061042
SN - 1366-5545
VL - 201
JO - Transportation Research Part E: Logistics and Transportation Review
JF - Transportation Research Part E: Logistics and Transportation Review
M1 - 104192
ER -