Robust and Adaptive Optimization of Carrier-Based Aircraft Operations Scheduling Considering Resource Degradation and Uncertainty

To address the issue of scheduling failure in carrier-based aircraft support operations under complex sea conditions caused by resource performance degradation and stochastic environmental disturbances, this paper proposes a robust and adaptive scheduling optimization framework. First, a Gamma stochastic process is introduced to explicitly model the dynamic degradation evolution path of support resources, and a non-linear mapping relationship coupling equipment status with failure probability is constructed. Second, the scheduling problem is formulated as a finite-horizon Markov Decision Process (MDP), and a deep reinforcement learning solution scheme based on the Proximal Policy Optimization (PPO) algorithm is designed. To enhance system survivability under extreme uncertainties, a scenario-based robust training mechanism is developed, enabling the policy to cover multiple degradation trajectories. Finally, a rolling-horizon rescheduling mechanism is incorporated to achieve adaptive feedback adjustment to real-time status deviations. Simulation results demonstrate that the proposed method significantly reduces the total support duration (makespan) while effectively enhancing equipment reliability and the disturbance resistance of scheduling plans.