An adaptive tabu search algorithm embedded with iterated local search and route elimination for the bike repositioning and recycling problem

Abstract

The bike repositioning and recycling problem (BRRP) is significant to develop a sustainable bike-sharing system and can effectively reduce the imbalance between demand and supply. This study investigates the static repositioning and recycling problem of a bike-sharing system, which is formulated as an integer linear programming model. The BRRP is a variant of the multi-depot simultaneous pickup and delivery problem with multi-commodity demand. To solve the proposed model, an adaptive tabu search (ATS) algorithm combined with six neighborhood structures is developed. Moreover, an iterated local search (ILS) and a route elimination operator are both embedded to reduce the number of routes. The performance of the proposed ATS is evaluated by comparing it with tabu search (TS) and variable neighborhood search (VNS). The experimental results show that the proposed algorithm performs better than TS and VNS in terms of the solution quality. The proposed ATS was used to analyze the bicycle system in New York City. Finally, a free solver is developed for the bike repositioning and recycling problem, which is named the BRRP Solver.