An Integrated Scheduling Method for Airport Special Vehicles Based on Multi-Objective Optimization

Airport ground support services are crucial for airport operations and flight punctuality, with integrated scheduling of special vehicles being a particularly vital component. To address issues regarding inefficiency and high costs in the scheduling process, a multi-objective optimizing model is proposed, considering factors such as energy consumption, efficiency, safety, and passenger satisfaction. The model is solved using an improved genetic algorithm (IGA), which enhances both population initialization and the evolutionary process compared to the standard genetic algorithm (SGA). Then, simulations are conducted with actual airport data to compare various scheduling algorithms. The results show that compared with SGA, the IGA-based method performs better in reducing scheduling costs, energy consumption, and flight delays by 15.03%, 28.52%, and 12.50%. Such improvement is 24.57%, 36.60%, and 7.34% compared to multi-objective genetic algorithm (MOGA). It means that the suggested scheduling approach effectively reduces costs and enhances efficiency, providing practical guidance for airport ground operations.