The resource scheduling optimization problem in predictive maintenance is a complex operational research challenge involving reasoning about stochastic failure models and the dynamic allocation of repair resources. In recent years, resource scheduling methods based on deep learning have been increasingly applied in this field, demonstrating promising performances. Among these, resource scheduling algorithms based on heterogeneous graphs have shown exceptional results in multi-objective optimization tasks. However, conventional graph neural networks primarily operate on binary relational graphs, which struggle to effectively utilize data in multi-relational settings, thereby limiting the scheduler’s performance. To address this limitation, this paper proposes a heterogeneous hypergraph-based resource scheduling algorithm for aircraft maintenance tasks to tackle the challenges of higher-order and many-to-many relationship processing inherent in traditional graph neural networks. Specifically, the proposed algorithm defines aircraft nodes and maintenance personnel nodes while introducing decision nodes and state nodes to construct hyperedges. It employs hypergraph convolution with a multi-head attention mechanism to learn the long-term value of decisions, followed by policy selection based on a Markov decision process. This method offers a lightweight, non-parametric dynamic scheduling solution capable of robust learning in highly stochastic environments. Comparative experiments conducted on three datasets of varying scales demonstrate that the proposed method outperforms both heuristic algorithms and existing deep learning methods in terms of its optimization performance on M1 and M2 metrics. Furthermore, it surpasses resource scheduling algorithms based on heterogeneous graph neural networks across multiple metrics.
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