Multiobjective Bike Repositioning in Bike-Sharing Systems via a Modified Artificial Bee Colony Algorithm

Abstract

With the expansion of the sharing economy, growing urban traffic, and increasing environmental pollution, bike-sharing systems (BSSs) are developing rapidly all over the world. A major operational issue in BSS is to reposition the bikes over time such that enough bikes and open parking slots are available to users. Especially during peak hours, it is essential to stabilize BSS in use. To cope with the issue, this article proposes a new approach integrating multiobjective optimization and a weighting factor based on the shortage event types of each station. In addition, the multiobjective artificial bee colony algorithm is modified according to the features of this work to find optimal solutions. The proposed approach is applied to the real-life repositioning of a BSS during peak hours to verify its feasibility and effectiveness. Also, the algorithm is compared with other frequently used multiobjective algorithms. For the comparative study, convergence metric and spacing are adopted to further measure the algorithm performance. The scalability of the proposed approach in addressing the multiobjective repositioning problems during peak hours is also verified by multiple trials. Note to Practitioners —This work deals with bike repositioning in bike-sharing systems (BSSs) during peak hours, which has major significance in the efficient operation of such systems. It builds a multiobjective optimization model and solves it through a modified multiobjective artificial bee colony algorithm. The existing single-objective optimization methods fail to solve the concerned problem. This work can find the optimal routes of the repositioning vehicles along with the number of desired parked bikes of corresponding stations. The experimental results indicate that the proposed method is highly effective and can greatly and readily help decision-makers better manage the BSS of a practical size.