An Aircraft Scheduling Optimization Model Based on Graph Reinforcement Learning

The aircraft scheduling process is often significantly affected by limited aircraft resources and the propagation of flight delays, resulting in considerable losses for airlines and passengers. This paper proposes an optimization model based on graph reinforcement learning to minimize aircraft resource utilization and reduce delay propagation to the greatest extent.First, a spatial temporal graph convolutional network is employed to analyze the propagation characteristics of flight delays, and a flight connection network is established based on the flight schedule and the propagation characteristics analyzed of flight delays. Next, the optimization objectives are set to minimize the used aircraft resource and the total propagation delays, with variable weights assigned to these objectives to meet the specific needs of different airlines. Finally, a graph reinforcement learning algorithm is developed for aircraft scheduling, and the graph reinforcement learning algorithm is compared with genetic algorithms and ant colony algorithms. Experimental results demonstrate that the graph reinforcement learning algorithm shows significant advantages both in terms of optimization performance and time efficiency. After optimizing the real-world aircraft scheduling plan, the number of aircraft used decreased by 2.21%, and propagation delays were reduced by 18.04%. The algorithm also achieved favorable results under different weight configurations, demonstrating strong adaptability.