Spatial-Temporal Inventory Rebalancing for Bike Sharing Systems With Worker Recruitment

Bike-sharing systems usually suffer from out-of-service events due to bike underflow or overflow. We propose to recruit workers to rebalance station loads. We partition the complex rebalancing problem in temporal and spatial domains. The temporal domain is divided into a sequence of slices with a fixed duration. In each slice, we allocate a pair of overflow/underflow stations to a worker such that the cost is minimized, which is NP-hard. A 3-approximation algorithm is proposed. We further investigate the worker shortage case and extend the matching algorithm to consider the number of unsatisfied users. Then, the configuration dynamic in the sequence of slices is captured by determining the rebalancing target for each rebalancing operation. We investigate heuristic approaches to minimize the total number of bike movements. Furthermore, we extend our scheme to dockless BSSs using clustering techniques. We simulate our algorithms on both real-world and synthetic datasets. Experiment results show that our approaches can reduce the average total detour per slice. In worker shortage, considering the number of unsatisfied users could improve the long-term performance of rebalancing. Besides, we find that our scheme could maintain worker satisfaction over multiple time slices, which indicates the sustainability of our rebalancing scheme.