Collaborative Scheduling Modeling for Departing Flights and Tow Trucks During Large-Scale Airport Delay Recovery

To mitigate flight delay losses and enhance airport ground operation efficiency, this study proposes a collaborative optimization model for departure flight sequencing and tow truck resource scheduling in the context of large-scale delay recovery. The model incorporates constraints such as runway departure capacity, limited tow truck availability, and apron operational rules. The objectives are to minimize flight recovery time and the costs of flight delays and to maximize tow truck resource utilization. We designed a multi-objective solving algorithm based on the Non-dominated Sorting Genetic Algorithm Ⅲ (NSGA-III) framework to address the problem. Simulation experiments using actual data from Beijing Daxing International Airport demonstrate that the proposed model and algorithm effectively reduce flight delay losses and operational costs while improving delay recovery efficiency during large-scale disruptions.