Hybrid Particle Swarm Optimization and Q-Learning for Airport Parking Space Allocation and Scheduling - Huang, Chunxin (Informatica, 2025)
With the rapid development of the aviation industry, the contradiction between the shortage of airport parking space resources and the continuous growth of air transportation demand has become increasingly prominent. Traditional parking space allocation and scheduling methods have been unable to cope with the increasingly complex and dynamic operating environment. To address this challenge, this paper proposes an airport parking space allocation and scheduling optimization model based on a meta-heuristic algorithm, combining the particle swarm optimization (PSO) algorithm with the Qlearning reinforcement learning method, aiming to improve the utilization efficiency of parking space resources and the level of intelligent scheduling. The method uses PSO to examine at the whole scheduling space and Q-learning to make adjustments to allocations depending on feedback from the environment in real time. In terms of research methods, we first constructed a mathematical model with multiple constraints and a comprehensive objective function, used the PSO algorithm to perform preliminary allocation of parking spaces, and introduced an adaptive mechanism to enhance the search capability. At the same time, the Q-learning model continuously optimizes scheduling decisions through interaction with the environment to ensure the optimal balance between the global and local. The hybrid approach enhances both global search and local optimization. The results show that this method is superior to individual PSO, Q-learning and traditional heuristic methods in multiple key indicators, including total scheduling cost, delay time, parking space utilization, algorithm convergence speed, number of scheduling conflicts, calculation time and successful scheduling rate. By coordinating factors such as cost, time and safety, the model can significantly improve airport operating efficiency, reduce flight delays and optimize resource allocation. With the CloudSim toolkit to run tests in a simulated cloud environment shows that our strategy cuts the average task latency by 15.2% and the overall scheduling cost by 12.5% compared to classic PSO and heuristic methods. The suggested approach works most effective when there are constraints on items like resource capacity, task deadlines, and energy use. The evaluation measures, which include makespan, cost, and delay time, show that the hybrid strategy works well and is strong.