Maintenance scheduling is a complex decision-making problem in the production domain, where a number of maintenance tasks and resources has to be assigned and scheduled to production entities in order to prevent unplanned production downtime. Intelligent maintenance strategies are required that are able to adapt to the dynamics and different conditions of production systems. The paper introduces a deep reinforcement learning approach for condition-oriented maintenance scheduling in flow line systems. Different policies are learned, analyzed and evaluated against a benchmark scheduling heuristic based on reward modelling. The evaluation of the learned policies shows that reinforcement learning based maintenance strategies meet the requirements of the presented use case and are suitable for maintenance scheduling in the shop floor.

翻译：在生产领域,维修时间安排是一个复杂的决策问题,必须把一些维修任务和资源分配给生产实体,并安排给生产实体,以防止意外生产停工,需要有能够适应生产系统的动态和不同条件的明智的维修战略,文件为流动线系统中注重条件的维修时间安排引入了深入强化学习方法,根据基于奖励模式的基准日程安排,学习、分析和评价了不同的政策。对学习政策的评价表明,加强学习的维护战略符合所述使用案例的要求,适合在商店楼层安排维修时间。