Joint pricing and launching strategy for bike-sharing enterprises based on Bertrand game

被引:0
|
作者
Tan C.-Q. [1 ,2 ]
Li J.-Z. [2 ]
Zhou L. [1 ]
机构
[1] School of Information, Beijing Wuzi University, Beijing
[2] School of Business, Central South University, Changsha
来源
Kongzhi yu Juece/Control and Decision | 2021年 / 36卷 / 07期
关键词
Bertrand game; Bike-sharing; Collaborative pricing; Differential pricing; Joint strategy;
D O I
10.13195/j.kzyjc.2019.1638
中图分类号
学科分类号
摘要
Based on the randomness of shared bicycle rental, this paper uses Bertrand game theory to construct a model of joint decision making of rental pricing and launching between shared bicycle enterprises in a random environment, and studies the optimal strategy combination of price and quantity of shared bicycle enterprises in two cases: differential pricing and collaborative pricing. The paper analyzes the impact of market potential demand and vehicle area coverage difficulty on the corporate strategy and operating profit. The research shows that: 1) In the case of differential pricing, when the demand for shared-bikes is satisfied with probability, there is a unique Nash equilibrium solution for the game between bike-sharing enterprises, and when the pricing power of low-price enterprises increases, the profits of both companies will gain growth; 2) In the case of collaborative pricing, the average rental price of the shared bicycle market is lower and increases with the potential demand of the market, but the growth rate is smaller than the differential pricing situation; 3) Collaborative pricing is conducive to maintaining the profit of bike-sharing enterprises when the market size is small, while differential pricing is conducive to restraining the excessive release of shared bikes when the market size expands. Copyright ©2021 Control and Decision.
引用
收藏
页码:1786 / 1792
页数:6
相关论文
共 11 条
  • [1] Medard de Chardon C, Caruso G, Thomas I., Bicycle sharing system 'success' determinants, Transportation Research, Part A: Policy and Practice, 100, pp. 202-214, (2017)
  • [2] Kabra A, Belavina E, Girotra K., Bike-share systems: Accessibility and availability, Management Science, 11, 9, pp. 3803-3824, (2019)
  • [3] Yan S Y, Lin J R, Chen Y C, Et al., Rental bike location and allocation under stochastic demands, Computers & Industrial Engineering, 107, pp. 1-11, (2017)
  • [4] Yang T H, Li Y, Zhou S M., System dynamics modeling of dockless bike-sharing program operations: A case study of mobike in Beijing, China, Sustainability, 11, 6, (2019)
  • [5] Lin J J, Wang N L, Feng C M., Public bike system pricing and usage in Taipei, International Journal of Sustainable Transportation, 11, 9, pp. 633-641, (2017)
  • [6] Banerjee S, Freund D, Lykouris T., Multi-objective pricing for shared vehicle systems
  • [7] Haider Z, Nikolaev A, Kang J E, Et al., Inventory rebalancing through pricing in public bike sharing systems, European Journal of Operational Research, 270, 1, pp. 103-117, (2018)
  • [8] Chen Y, Wang D, Chen K H, Et al., Optimal pricing and availability strategy of a bike-sharing firm with time-sensitive customers, Journal of Cleaner Production, 228, pp. 208-221, (2019)
  • [9] Cheng X, Gao Y., The optimal monthly strategy pricing of free-floating bike sharing platform, Modern Economy, 9, 2, pp. 318-338, (2018)
  • [10] Pang H Q, Liu Z Y., A summary of the development process and countermeasures of China's shared bicycle phase, 4th International Conference on Social Sciences and Economic Development (ICSSED 2019), pp. 147-150, (2019)