An improved QPSO algorithm for multi‐objective airport bus and driver scheduling with time windows

Efficient scheduling of vehicles and drivers is crucial for airport bus services. Traditional methods face challenges such as separate services for commuter and shuttle trips and fixed vehicle‐driver pairings, leading to resource wastage and uneven driver workloads. This study addresses a multi‐objective optimization problem for scheduling airport buses and drivers, using time windows to reduce the chance of passengers missing the bus due to delays like baggage claim. It promotes cooperative scheduling across units and supports flexible vehicle‐driver pairings, aiming to improve utilization and balance workloads. We developed a mixed integer linear programming model and designed an improved hybrid quantum particle swarm optimization algorithm to achieve high‐quality solutions efficiently. The performance of this algorithm was benchmarked against the GAMS solver and other existing algorithms. Applied to a real‐world scenario at Xianyang Airport in China, our approach significantly reduced costs and balanced workloads among drivers, underscoring its benefits for airport bus operations.