The interrupting swap-allowed blocking job shop problem (ISBJSSP) is a complex scheduling problem that is able to model many manufacturing planning and logistics applications realistically by addressing both the lack of storage capacity and unforeseen production interruptions. Subjected to random disruptions due to machine malfunction or maintenance, industry production settings often choose to adopt dispatching rules to enable adaptive, real-time re-scheduling, rather than traditional methods that require costly re-computation on the new configuration every time the problem condition changes dynamically. To generate dispatching rules for the ISBJSSP problem, a method that uses graph neural networks and reinforcement learning is proposed. ISBJSSP is formulated as a Markov decision process. Using proximal policy optimization, an optimal scheduling policy is learnt from randomly generated instances. Employing a set of reported benchmark instances, we conduct a detailed experimental study on ISBJSSP instances with a range of machine shutdown probabilities to show that the scheduling policies generated can outperform or are at least as competitive as existing dispatching rules with predetermined priority. This study shows that the ISBJSSP, which requires real-time adaptive solutions, can be scheduled efficiently with the proposed machine learning method when production interruptions occur with random machine shutdowns.

翻译：中断可交换阻塞作业车间调度问题（ISBJSSP）是一种复杂的调度问题，通过同时考虑存储容量不足和不可预见的生产中断，能够真实模拟众多制造规划与物流应用场景。在面临机器故障或维护导致的随机中断时，工业制造环境往往采用调度规则来实现自适应的实时重调度，而非每次问题条件动态变化时都需要对新的配置进行昂贵重新计算的传统方法。为生成ISBJSSP问题的调度规则，本文提出了一种基于图神经网络与强化学习的方法。ISBJSSP被形式化为马尔可夫决策过程，通过近端策略优化从随机生成的实例中学习最优调度策略。基于一组已公开的基准测试实例，我们在不同机器停机概率条件下的ISBJSSP实例上进行了详细的实验研究，结果表明所生成的调度策略能够优于或至少与现有预设优先级调度规则具有同等竞争力。本研究证明，当生产中断伴随随机机器停运时，所提出的机器学习方法能够高效调度需要实时自适应解决方案的ISBJSSP问题。