In scheduling problems common in the industry and various real-world scenarios, responding in real-time to disruptive events is essential. Recent methods propose the use of deep reinforcement learning (DRL) to learn policies capable of generating solutions under this constraint. The objective of this paper is to introduce a new DRL method for solving the flexible job-shop scheduling problem, particularly for large instances. The approach is based on the use of heterogeneous graph neural networks to a more informative graph representation of the problem. This novel modeling of the problem enhances the policy's ability to capture state information and improve its decision-making capacity. Additionally, we introduce two novel approaches to enhance the performance of the DRL approach: the first involves generating a diverse set of scheduling policies, while the second combines DRL with dispatching rules (DRs) constraining the action space. Experimental results on two public benchmarks show that our approach outperforms DRs and achieves superior results compared to three state-of-the-art DRL methods, particularly for large instances.

翻译：在工业及各类实际场景中常见的调度问题中，对干扰事件进行实时响应至关重要。近期方法提出采用深度强化学习（DRL）来学习能够在上述约束下生成解决方案的策略。本文旨在提出一种新的DRL方法，用于求解柔性作业车间调度问题，特别是大规模实例。该方法基于异构图神经网络，将问题表示为信息更丰富的图结构。这种新颖的问题建模方式增强了策略捕捉状态信息的能力，并提升了其决策能力。此外，我们引入了两种新颖方法来提升DRL方法的性能：第一种是生成多样化的调度策略集，第二种是将DRL与约束动作空间的调度规则（DRs）相结合。在两个公开基准上的实验结果表明，我们的方法优于调度规则，并与三种最先进的DRL方法相比取得了更优的结果，尤其是在大规模实例上。