Data-Driven Robust Integrated Car-Sharing Scheduling with Heterogeneous Vehicle Considering Uncertain Dispatcher and Relocation Times

<jats:p>Car-sharing systems, as an important form of shared transport, face supply-demand imbalance and complex scheduling challenges. Heterogeneous vehicle can better meet diverse customer demands by offering vehicles with different capacities. Meanwhile, uncertainty in dispatcher movement time and vehicle relocation time may affect the reliability of scheduling plans. To address these issues, this paper focuses on a one-way station-based car-sharing problem, considering integrated scheduling, heterogeneous vehicle, uncertain dispatcher movement time and vehicle relocation time simultaneously. We define the uncertainty sets for dispatcher movement time and vehicle relocation time based on historical data from the Taxi &amp;amp; Limousine Commission (TLC) in Queens, New York by data-driven method. A robust optimization model is developed to improve order fulfillment and scheduling stability. We develop a hybrid approach that combines adaptive large neighborhood search and a genetic algorithm (ALNS/GA), and incorporates robustness into solution decoding and fitness evaluation under the data-driven uncertainty sets. Numerical experiments based on real TLC data show that the integrated scheduling system reduces average costs by 10.69\% compared with non-integrated systems, while the integrated robust scheduling system further improves the order fulfillment rate by 0.99\%. Sensitivity analysis also offers useful managerial insights.</jats:p>