The Flexible Job Shop Scheduling Problem (FJSSP) has been extensively studied in the literature, and multiple approaches have been proposed within the heuristic, exact, and metaheuristic methods. However, the industry's demand to be able to respond in real-time to disruptive events has generated the necessity to be able to generate new schedules within a few seconds. Among these methods, under this constraint, only dispatching rules (DRs) are capable of generating schedules, even though their quality can be improved. To improve the results, recent methods have been proposed for modeling the FJSSP as a Markov Decision Process (MDP) and employing reinforcement learning to create a policy that generates an optimal solution assigning operations to machines. Nonetheless, there is still room for improvement, particularly in the larger FJSSP instances which are common in real-world scenarios. Therefore, the objective of this paper is to propose a method capable of robustly solving large instances of the FJSSP. To achieve this, we propose a novel way of modeling the FJSSP as an MDP using graph neural networks. We also present two methods to make inference more robust: generating a diverse set of scheduling policies that can be parallelized and limiting them using DRs. We have tested our approach on synthetically generated instances and various public benchmarks and found that our approach outperforms dispatching rules and achieves better results than three other recent deep reinforcement learning methods on larger FJSSP instances.

翻译：柔性作业车间调度问题（FJSSP）在文献中已被广泛研究，学者们提出了多种基于启发式、精确式和元启发式方法的方案。然而，工业界对实时应对突发事件的响应需求，催生出在数秒内生成新调度方案的必要性。在此约束条件下，现有方法中仅派工规则（DRs）能够生成调度方案，但其质量仍有提升空间。为改进结果，近期研究将FJSSP建模为马尔可夫决策过程（MDP），并采用强化学习创建将工序分配给机器的优化策略。尽管如此，在现实场景中常见的大规模FJSSP实例上仍存在改进空间。因此，本文旨在提出一种能鲁棒求解大规模FJSSP实例的方法。为此，我们提出了一种基于图神经网络将FJSSP建模为MDP的新范式，并提出了两种增强推理鲁棒性的方法：生成可并行化的多样化调度策略集，以及通过DRs对策略集进行约束。我们在合成生成的数据集和多个公开基准上进行了测试，结果表明：相较于派工规则，我们的方法性能更优；且在大规模FJSSP实例上，该方法优于其他三种最新深度强化学习方法。