Operator- and user-based rebalancing strategy for bike-sharing systems

Abstract

Leveraging their networks, bike rental companies usually provide customers with services for renting and returning bikes at different bike stations. Over time, however, rental networks may encounter problems with unbalanced bike stocks. The potential imbalance between supply and demand at bike stations may result in lost sales for stations with relatively high demand and underutilization for stations with relatively low demand. This paper proposed a constrained mixed-integer programming model that uses operator-based redistribution and user-based price approach to rebalance bikes across bike stations. This paper aims to maximize total profit over a planning horizon by determining operator-based bike transfers and dynamic pricing. The proposed model is a non-deterministic polynomial-time problem, and thus, a heuristic was developed based on linear programming and evolutionary computation to perform model solving. Numerical experiments reveal that the proposed method performed better than Lingo, a well-known commercial software. Sensitivity analyses were also performed to investigate the impact of changes in system parameters on computational results.