A heuristic approach to bicycle repositioning problems with dynamic pricing

Abstract

Bicycle-sharing systems are commonly established at geographically dispersed locations to create their rental service networks. To provide customers with flexibility and convenience, bicycle-sharing systems commonly allow them to pick up bicycles from one station and return them to a different one. However, allowing customers to return their rented bicycles to different stations can possibly lead to an imbalance in the bicycle rental network. One of the approaches to overcome the bicycle imbalance problem is to apply dynamic pricing to motivate consumers to return the rented bicycles to stations without a sufficient number of bicycles. This study developed a constrained dynamic pricing model to address the bicycle imbalance problem. Moreover, this study aimed to maximize the total revenues over a planning horizon through dynamic pricing strategies. We identify some necessary conditions for optimal sale prices. Using these conditions, we develop a heuristic algorithm based on linear programming and an evolutionary algorithm to efficiently produce comprise solutions. The proposed model and solution procedure were applied to analyze the bicycle system in Taiwan. Sensitivity analyses were also conducted to investigate the effects of various system parameters.