An enhanced artificial bee colony algorithm for the green bike repositioning problem with broken bikes

• Published in: Transportation research. Part C, Emerging technologies Vol. 125; p. 102895
• Main Authors: Wang, Yue, Szeto, W.Y.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2021
• Subjects: Artificial bee colony algorithm; Broken bikes; Emissions; Green bike repositioning problem; Artificial bee colony algorithm; Green bike repositioning problem; Broken bikes; Emissions
• ISSN: 0968-090X; 1879-2359; 1879-2359
• DOI: 10.1016/j.trc.2020.102895

•This study tackles a static green bike repositioning problem with broken bikes.•We proposed a hybrid heuristic based on the Enhanced Artificial Bee Colony algorithm.•We use the Enhanced Artificial Bee Colony algorithm to generate the vehicle route.•We propose a linear program and a heuristic to compute loading quantities at visited stops.•The proposed hybrid heuristic outperforms two benchmark methods. The Bike Repositioning Problem (BRP) has raised many researchers’ attention in recent years to improve the service quality of Bike Sharing Systems (BSSs). It is mainly about designing the routes and loading instructions for the vehicles to transfer bikes among stations in order to achieve a desirable state. This study tackles a static green BRP that aims to minimize the CO2 emissions of the repositioning vehicle besides achieving the target inventory level at stations as much as possible within the time budget. Two types of bikes are considered, including usable and broken bikes. The Enhanced Artificial Bee Colony (EABC) algorithm is adopted to generate the vehicle route. Two methods, namely heuristic and exact methods, are proposed and incorporated into the EABC algorithm to compute the loading/unloading quantities at each stop. Computational experiments were conducted on the real-world instances having 10–300 stations. The results indicate that the proposed solution methodology that relies on the heuristic loading method can provide optimal solutions for small instances. For large-scale instances, it can produce better feasible solutions than two benchmark methodologies in the literature.