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Chinese Journal of Management Science ›› 2022, Vol. 30 ›› Issue (4): 275-286.doi: 10.16381/j.cnki.issn1003-207x.2020.1026

• Articles • Previous Articles    

Modeling and Optimizing Method for Rebalancing the Dock-less Bicycles based on Order Data Analysis

LIU Ming1, XU Xi-fen1, NING Jing1, CAO Jie2   

  1. 1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China;2. School of Management Engineering, Xuzhou University of Technology, Xuzhou 221018, China
  • Received:2020-06-01 Revised:2020-07-29 Online:2022-04-26 Published:2022-04-26
  • Contact: 曹杰 E-mail:cj@amss.ac.cn

Abstract: The dockless sharing bicycle system breaks the limitation of traditional public sharing bicycle system because the latter relies on the fixed stations to provide travel service. Therefore, it greatly improves the user's riding experience. However, how to rebalance the dockless bicycles, which are randomly distributed on the whole area, has become a big problem faced by decision makers. The data-driven operation research method provides the possibility to solve the above problem. In this study, the large-scale order dataset of the dockless sharing bicycles is analyzed to explore the travel rules of the users. Then the collection points and the release points are defined. A newsboy model is referenced to construct the innovative revenue function when releasing dockless bicycles at the release points. After that, an optimization model for rebalancing the dockless sharing bicycles by considering both pick-up and delivery aspects is proposed. The test results demonstrate that the proposed method can provide effective decision-making suggestions for rebalancing the dockless sharing bicycles from the perspectives of the maximum loading capacity, the unit distance cost, the driving distance limitation, the number of collection points and the number of bicycles in the corresponding collection points.

Key words: dockless sharing bicycle;data analysis;rebalancing scheduling;vehicle routing problem;operation optimization

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