Aircraft Line Maintenance Scheduling Using Simulation and Reinforcement Learning

This paper presents a reinforcement learning (RL) algorithm prototype to solve the aircraft line maintenance scheduling problem. The Line Maintenance Scheduling Problem (LMSP) is concerned with scheduling a set of maintenance tasks during an aircraft’s ground time. To address this problem, we introduce a novel LMSP method combining a hybrid simulation model and reinforcement learning to schedule maintenance tasks at multiple airports. Initially, this paper briefly reviews the existing literature on optimization-based and AI-enhanced aircraft maintenance scheduling. Secondly, the novel reinforcement learning LMSP method is introduced, evaluated using industry data, and compared with optimization-based LMSP solutions. Our experiments demonstrate that the LMSP method using reinforcement learning is capable of identifying near-optimal policies for scheduling line maintenance jobs when compared to the exact and heuristics-based methods. The proposed model provides an excellent foundation for future studies on AI-enhanced scheduling problems.