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:中断互换允许的封堵工作商店问题(ISBJSSP)是一个复杂的时间安排问题,能够通过解决仓储能力不足和意外生产中断的问题,现实地模拟许多制造业规划和后勤应用,解决许多储存能力不足和意外生产中断的问题。如果机器故障或维修造成随机中断,工业生产环境往往选择采用发送规则,以便能够适应性、实时重订时间表,而不是传统方法,每次问题状况发生变化,都需要对新配置进行费用高昂的重估。为了产生对ISBJSSP问题的发送规则,提出了使用神经网络图和强化学习的方法。ISBJSSP是作为Markov决定程序制定的。使用准政策优化,从随机产生的事例中学习最佳的时间安排政策。我们使用一套报告的基准实例,对ISBJSP案例进行详细的实验性研究,通过一系列机器关闭的概率,表明所制定的时间安排政策可以超过或至少像现有的发送规则那样具有竞争力。这项研究表明,ISBJSSP需要实时的机器停机率,在机器停机率上学习机器停产的方法。