Scheduling for Carrier Aircraft Hangar Maintenance in Wave Missions Involving Chance Constraints

In modern naval operations, the efficient scheduling of carrier aircraft hangar maintenance under wave-mission constraints is critical for sustaining fleet readiness and combat effectiveness. Existing methods often lack adaptability to dynamic disruptions and fail to explicitly account for operational uncertainty. This paper presents an integrated optimization and rescheduling framework tailored for carrier aircraft hangar maintenance in wave missions. First, a Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP) model is developed, incorporating chance constraints to explicitly address the probabilistic nature of wave-launch timing. The model aims to simultaneously maximize wave-mission completion and fleet wave availability, ensuring robust decision-making under uncertainty. Second, a priority-based encoding structure and a dual-layer decoding mechanism are designed to efficiently represent the scheduling problem. Building on this encoding, a Hybrid Particle Swarm Optimization (HPSO) algorithm is proposed, integrating genetic algorithm-inspired crossover and elite replacement strategies to enhance global exploration and convergence speed. Furthermore, a rapid rescheduling strategy leveraging the same encoding architecture is introduced to seamlessly adjust schedules in response to unexpected events such as personnel reassignments or equipment failures. Comprehensive simulations demonstrate that the proposed approach significantly improves scheduling flexibility and robustness. Compared to traditional algorithms, HPSO consistently generates high-quality solutions within seconds, maintaining optimal wave-task completion and fleet availability even after disruptions. This research provides a practical and intelligent decision-support tool for carrier aircraft maintenance scheduling, with potential applications extending to other resource-constrained project environments.