Dynamic Matching of Uncertain Demand with Uncertain Supply for Bike Sharing Systems
DOI:
https://doi.org/10.23055/ijietap.2019.26.6.4077Keywords:
Bike rebalancing, Uncertain demand and uncertain supply, Deep learning, Bike sharing systemsAbstract
In Operations Management, most production and inventory models incorporate either demand uncertainty or supply uncertainty as input. When both uncertainties are involved, they are treated separately with an intermediary inventory stock and they are not directly linked in analysis. In this paper, we address a new problem of matching uncertain demand with uncertain supply directly. The problem arises in bike sharing systems that feature uneven and dynamic flow of bikes across bike stations. Besides periodical rebalancing, dynamic rebalancing of bikes is crucial to upholding service quality but is a costly transportation operation. We present an analysis of the solution characteristics and show that it is frequent to have multiple optimal solutions which give rise to dispersed transfers of bikes. As dynamic rebalancing is aimed at resolving the imbalance problem selectively at critical points, we construct a mathematical programming model to evaluate the utility of each transfer to facilitate prioritizing candidate transfers. By analyzing the numerical results of the mathematical programming and deep learning models, we conclude that rule-based heuristic methods are suitable for dynamic rebalancing of bike sharing systems.
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