Stochastic fleet deployment models for public bicycle rental systems

Shangyao Yan, Chung Cheng Lu, Min Hung Wang

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


This paper presents two stochastic bike deployment (SBD) models that determine the optimal number of bicycles allocated to each station in a leisure-oriented public bicycle rental system with stochastic demands. The SBD models represent the stochastic demands using a set of scenarios with given probabilities. A multilayer bike-flow time-space network is constructed for developing the models, where each layer corresponds to a given demand scenario and effectively describes bicycle flows in the spatial and temporal dimensions. As a result, the models are formulated as the integer multi-commodity network flow problem, which is characterized as NP-hard. We propose a heuristic to efficiently obtain good quality solutions for large-size model instances. Test instances are generated using real data from a bicycle rental system in Taiwan to evaluate the performance of the models and the solution algorithm. The test results show that the models can help the system operator of a public bicycle system make effective fleet deployment decisions.

Original languageEnglish
Pages (from-to)39-52
Number of pages14
JournalInternational Journal of Sustainable Transportation
Issue number1
StatePublished - 2 Jan 2018


  • Bicycle rental
  • fleet allocation
  • multi-commodity network flows
  • public bicycles
  • time-space networks


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