A Matheuristic Multi-Start Algorithm for a Novel Static Repositioning Problem in Public Bike-Sharing Systems

TL;DR

This paper presents a novel matheuristic multi-start algorithm for the static repositioning problem in bike-sharing systems, effectively handling operational complexities and delivering high-quality solutions efficiently.

Contribution

It introduces a new matheuristic approach combining randomized multi-start and integer programming for complex bike repositioning problems.

Findings

Algorithm achieves high-quality solutions quickly.

Effective in real-world data scenarios.

Competitive results on simplified problems.

Abstract

This paper investigates a specific instance of the static repositioning problem within station-based bike-sharing systems. Our study incorporates operational and damaged bikes, a heterogeneous fleet, and multiple visits between stations and the depot. The objective is to minimize the weighted sum of the deviation from the target number of bikes for each station, the number of damaged bikes not removed, and the total time used by vehicles. To solve this problem, we propose a matheuristic approach based on a randomized multi-start algorithm integrated with an integer programming model for optimizing the number of operatives and damaged bikes that will be moved between stations and/or the depot (loading instructions). The algorithm's effectiveness was assessed using instances derived from real-world data, yielding encouraging results. Furthermore, we adapted our algorithm to a simpler problem studied in the literature, achieving competitive outcomes compared to other existing methods. The experimental results in both scenarios demonstrate that this algorithm can generate high-quality solutions within a short computational time.