The dynamic bike repositioning problem with battery electric vehicles and multiple charging technologies

Abstract

The bike-repositioning problem (BRP) primarily involves determining the routes and loading instructions for a fleet of vehicles that transport bikes between stations in a bike sharing system (BSS), to mitigate the mismatch between the demand for and supply of public bikes. However, the use of fossil-fueled vehicles for this repositioning task generates pollutants and greenhouse gases, which harm the environment. The use of battery electric vehicles (BEVs) instead of fossil-fueled vehicles for repositioning bikes can mitigate this negative environmental impact. On the other hand, user demand for bikes is dynamic during the daytime. Therefore, this study addresses the dynamic BRP with battery electric vehicles. Multiple charging technologies are available at charging stations to allow repositioning vehicles to recharge en-route at different speeds and costs. The objective is to solve the problem by minimizing the weighted sum of the penalty costs of unmet user demand and the charging costs of repositioning vehicles. A rolling horizon framework is adopted to incorporate the revealed inventory levels at bike stations, the BEV load of bikes, and the battery energy levels of BEVs at regular intervals. An artificial bee colony algorithm with an embedded dynamic programming method for computing the loading instructions is proposed to generate solutions. Computational experiments are conducted on a real-world BSS, and three aspects of the problem properties are analyzed: the horizon settings, the various penalties for failed rentals and returns, and the charging-related settings. The results provide practical insights into the use of BEVs for daily bike-repositioning tasks in BSSs.