Intelligent decision making for fleet maintenance based on DDQN

Toimprove fleet maintenance efficiency under limited resource constraints, this paper proposes an aircraft maintenance guarantee process based on the Double Deep Q Network algorithm (DDQN) algorithm, which coordinates and optimizes the maintenance elements, shortens the total maintenance time, and improves the fleet integrity rate. Firstly, build the simulation maintenance guarantee environment and the reinforcement learning c for decision-making. Then, define the Q value function of the associated maintenance cost, run the agent in the simulation environment, improve the parameter settings of the agent, and carry out interactive learning. Finally, verify the optimization effect of the reinforcement learning, and evaluate the effectiveness of the algorithm in comparison with other decision making methods. The results show that the algorithm is optimized for maintenance decisions in dynamic environments.