A Rolling-Horizon Strategy for Dynamic Rebalancing of Free-Floating Bike-Sharing Systems

Abstract

This paper presents a novel hybrid rolling-horizon strategy to address the dynamic rebalancing problem in large-scale, free-floating bike-sharing systems (FFBSSs). The problem involves determining the rebalancing routes and the quantity of bikes to be loaded and unloaded at various locations to minimize vehicle rebalancing costs and to reduce the degree of imbalance in the system. The proposed strategy consists of two stages: a preplanning stage that solves the preplanned scheme based on the historical data, and a real-time stage that compute a dynamic rebalancing operation over a rolling horizon. We propose the candidate grid cell to decrease solution time and enhance solution quality in the real-time stage. The preplanned scheme is adjusted during the real-time stage to maintain its validity in dynamic environments. By controlling both temporal and spatial parameters, the approach achieves a balance between solution complexity and accuracy. A numerical study based on the Shanghai FFBSS demonstrates that the proposed strategy outperforms two existing methods in terms of solution quality and CPU time, highlighting its potential for effectively managing dynamic rebalancing in FFBSSs.