Rental bike location and allocation under stochastic demands

In this research, we develop four planning models for leisure-oriented public bicycle rental systems under deterministic and stochastic demands, respectively. Time-space network models are employed to determine the locations of bike rental stations, bike fleet allocation and bike routing. These models are formulated as mixed integer programs that are characterized as NP-hard. While the two deterministic models can be solved directly using CPLEX, a threshold-accepting-based heuristic is developed to efficiently solve the stochastic models. Finally, numerical tests using operating data from the New Taipei City Public Bike program are performed to evaluate the models and the solution algorithm. The test results show that the proposed models and solution algorithm are useful for practices.