Hybrid Scheduling Optimisation for Bike‐Sharing Systems With User Participation: Supply‐Demand Matching and Collaborative Mechanisms

ABSTRACT

Resource rebalancing in bike‐sharing systems is costly and inefficient under centralised scheduling. This study develops a user‐operator collaborative hybrid scheduling framework incentivising user participation in small‐scale bike transfers to complement operator services. A supply‐demand matching‐driven two‐tier incentive mechanism uses weighted scoring based on demand gaps and spatial proximity for station matching, with a three‐component cost structure. Multi‐dimensional suitability assessment determines optimal user‐operator station partitioning through demand scale, economic feasibility and matching feasibility screening. A mixed‐integer programming model with flow conservation, capacity, time window and mileage constraints is solved by a hybrid genetic algorithm enhanced with large neighbourhood search. Validation using real data from 78 stations in Xiamen shows the hybrid solution reduces vehicle usage by 25%, travel distance by 26.7% and operator costs by 23.8%, achieving 16.6% total cost savings after deducting user incentives. Under the hybrid problem setting, the operator‐routing objective is 25.2% lower and convergence approximately 35% faster, reflecting a reduced routing burden from user assignment rather than a pure algorithmic advantage. Sensitivity analyses confirm economic robustness under imperfect user participation and demand uncertainty. The proposed framework is an operational/incentive‐based dispatching model, with detailed behavioural response modelling identified as future work.