A Hybrid Reverse Learning Particle Swarm Optimization Method for Aircraft Maintenance Scheduling Based on the Resource-Constrained Project Scheduling Problem Model

Aircraft maintenance scheduling is a critical task in air transportation and national defense security, characterized by complex multi-step procedures, strict precedence dependencies, and multi-resource constraints involving personnel skills and equipment availability. Traditional scheduling methods and standard metaheuristic algorithms often suffer from insufficient model adaptability, poor population diversity, premature convergence, and complex encoding schemes that require frequent feasibility checks. To address these challenges, this paper proposes a comprehensive optimization framework based on the Resource-Constrained Project Scheduling Problem (RCPSP) model. A decimal priority-based encoding method is introduced to replace traditional integer permutation encoding, significantly reducing computational complexity and enhancing search space continuity. Furthermore, an improved hybrid Particle Swarm Optimization algorithm integrating reverse learning and partial random operations (RL-PSO) is developed. The reverse learning mechanism expands the global search space by generating reverse particles, while partial random operations maintain population diversity and prevent premature convergence. The proposed framework converts priority encoding into feasible schedules through a priority sorting and left-shift resource allocation strategy. Simulation experiments on maintenance tasks involving up to 50 aircraft demonstrate that RL-PSO achieves optimization accuracy of 332 min, convergence speed of 92.07 s, and stability of 2.8843 min in standard deviation, which are superior compared to standard PSO, Simulated Annealing, and Teaching–Learning-Based Optimization combined with the serial schedule generation scheme (SSGS). The method effectively balances global exploration and local exploitation, making it suitable for complex, large-scale aircraft maintenance scenarios. Future work will extend the framework to multi-objective optimization and dynamic scheduling environments.