Integrated rebalancing and faulty bike recycling in dockless bike-sharing systems: a hybrid metaheuristic approach with dynamic clustering

Bike-sharing systems have become increasingly popular worldwide due to their affordability, efficiency, health benefits, and environmental friendliness. However, dockless bike-sharing systems face significant bike imbalance and faulty bike accumulation problems that negatively affect service quality, revenue, and user safety. This article addresses the integrated rebalancing and faulty bike recycling problem using a vehicle routing framework with partitioned vehicle capacity. For small-scale validation, the model is solved exactly using the CPLEX optimizer tool on 38 Nanjing stations. To enable large-scale applicability, we develop a hybrid genetic algorithm (GA)–adaptive large neighborhood search (ALNS) metaheuristic with cluster-first, route-second decomposition. Experiments show near-optimal solutions (gaps <3.2%) for 200-station instances within practical timeframes. Dynamic clustering reduces routing distance by 6.5% versus static approaches, while faulty bike integration adds only 8–12% computational overhead yet eliminates separate collection trips. Results confirm the framework effectively minimizes user dissatisfaction and operational costs while ensuring efficient faulty bike recycling.