Dynamic aircraft maintenance scheduling with Graph Neural Network integrated deep reinforcement learning

Abstract

Industrial aircraft maintenance policies still rely on rigid, static slot structures that limit operational efficiency. Existing research has mainly addressed task (re-)allocation within a pre-defined number and duration of slots, while direct slot planning (jointly determining slot duration and allocation) remains difficult due to its combinatorial complexity. We propose a graph-based, reinforcement-learning framework that sequentially constructs maintenance task packages within a fixed planning horizon. A Graph Neural Network (GNN) encodes relations among tasks to inform a deep reinforcement learning (DRL) policy, which learns to group tasks into slots efficiently. This framework is trained and evaluated on operational data from a major European airline and benchmarked against a fixed-time Mixed-Integer Linear Programming (MILP) benchmark and the current industry practice. The results demonstrate that the model rapidly generates feasible schedules, and quantify trade-offs among interval spillage, panel reuse, and drop-out tasks relative to these benchmarks. Our model is able to decrease task spillage and increase panel reuse consistently. Finally, as the reward weights can be tuned to reflect an airline’s operational preferences, this framework provides a flexible decision-support tool for dynamic aircraft maintenance task packaging.