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Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information

Author

Listed:
  • Ruijing Wu

    (Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Shaoxuan Liu

    (Ningbo Supply Chain Innovation Institute China, MIT Global Supply Chain and Logistics Excellence (SCALE) Network, Ningbo 315000, China)

  • Zhenyang Shi

    (Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China)

Abstract

Free-float bike-sharing (FFBS) systems have increased in popularity as a sustainable travel mode in recent years, especially in the urban areas of China. Despite the convenience such systems offer to customers, it is not easy to maintain an effective balance in the distribution of bikes. This study considers the dynamic rebalancing problem for FFBS systems, whereby user-based tactics are employed by incentivizing users to perform repositioning activities. Motivated by the fact that the problem is frequently faced by FFBS system operators entering a new market with limited information on travel demand, we adopt the ranking and selection approach to select the optimal incentive plan. We describe the system dynamics in detail, and formulate a profit maximization problem with a constraint on customer service level. Through numerical studies, we first establish that our procedure can select the optimal incentive plan in a wide range of scenarios. Second, under our incentive plan, the profit and service level can be improved significantly compared with the scenario without incentive provision. Third, in most cases, our procedure can achieve the optimal solution with a reasonable sample size.

Suggested Citation

  • Ruijing Wu & Shaoxuan Liu & Zhenyang Shi, 2019. "Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information," Sustainability, MDPI, vol. 11(11), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3088-:d:236087
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    References listed on IDEAS

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    Cited by:

    1. Mohammed Elhenawy & Hesham A. Rakha & Youssef Bichiou & Mahmoud Masoud & Sebastien Glaser & Jack Pinnow & Ahmed Stohy, 2021. "A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems," Sustainability, MDPI, vol. 13(23), pages 1-19, December.
    2. Li, Xiang & Wang, Xianzhe & Feng, Ziyan, 2024. "Dynamic repositioning in bike-sharing systems with uncertain demand: An improved rolling horizon framework," Omega, Elsevier, vol. 126(C).
    3. Dandan Xu & Yang Bian & Jian Rong & Jiachuan Wang & Baocai Yin, 2019. "Study on Clustering of Free-Floating Bike-Sharing Parking Time Series in Beijing Subway Stations," Sustainability, MDPI, vol. 11(19), pages 1-20, September.
    4. Chen, Qingxin & Ma, Shoufeng & Li, Hongming & Zhu, Ning & He, Qiao-Chu, 2024. "Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    5. Zhang, Yuhan & Shao, Yichang & Bi, Hui & Aoyong, Li & Ye, Zhirui, 2023. "Bike-sharing systems rebalancing considering redistribution proportions: A user-based repositioning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).

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