Maintenance stands are the scarcest maintenance resources, providing the essential space and facilities for aircraft maintenance. To expand the maintenance market, maintenance, repair and overhaul companies urgently need to achieve a more rational intelligent aircraft maintenance stand schedule. This paper aims to examine aircraft maintenance stand scheduling problem to enhance safety, efficiency and profitability.
A new heterogeneous graph model is first constructed to represent the scheduling problem, incorporating two types of nodes: maintenance item nodes and maintenance stand nodes. Second, the authors propose a novel graph attention network architecture to enable targeted embeddings for both nodes and arcs. Then, an end-to-end proximal policy optimization algorithm with an actor-critic style is designed to interact with the environment iteratively, leveraging a vectorized value function.
The extensive experimental results validate the performance of the proposed method, demonstrating its superiority over the state-of-the-art in terms of high-quality solutions, online computation time and generalization capability. Moreover, ablation studies on synthetic instances demonstrate that the proposed method can significantly reduce the relative scheduling error by at least 20.89%.
To enhance safety, efficiency and profitability in the maintenance process, the authors use an end-to-end deep reinforcement learning method to address aircraft maintenance stand scheduling problem.
