A GPR-ACO hybrid algorithm: solving the Airport Baggage Transport Vehicles Scheduling problem

The airport baggage transportation task plays a crucial role in airport ground tasks. In order to improve the quality and efficiency of airport services, airport baggage transport vehicles are required to arrive at flight unloading points timely and execute baggage transport tasks efficiently. This paper investigates the Airport Baggage Transport Vehicles Scheduling (ABTVS) problem, considering the real-time updates of flight arrival times and the uncertainty of completion times of baggage transportation tasks. Inspired by the Vehicle Routing Problem with Time Window (VRPTW), a Chance-Constrained Programming (CCP) model is proposed to improve passenger satisfaction and the airport baggage transport vehicle utilization. To solve the ABTVS problem, a hybrid algorithm based on the Gaussian Process Regression (GPR) algorithm and the Ant Colony Optimization (ACO) algorithm is developed.