Abstract This paper describes and examines thoroughly a stochastic production/inventory system that produces a single type of products. During the production process, the system is affected by several deterioration failures. It is restored to its initial and previous deterioration state by repair and maintenance activities. Both maintenance and repair duration are assumed as exponential random variables. Moreover, the quality of the manufactured products is assumed to be affected by the current deterioration level of the system. The aim of this paper is to find the optimal trade-off between conflicting performance metrics for the optimization of the total expected profit of the system. To tackle such optimization problems, researchers frequently employ Dynamic Programming. This method, though, is not appropriate for the addressed problem due to complexity reasons. To this end, a Reinforcement Learning-based approach is proposed in order to obtain the optimal joint production, maintenance and product quality control policies. To the authors’ knowledge, the proposed approach is novel and there are few examples of such implementation in the academic literature.

中文翻译：

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具有退化故障的制造系统的生产维护和质量控制相结合的强化学习

摘要 本文详细描述和检验了生产单一类型产品的随机生产/库存系统。在生产过程中，系统会受到多次恶化故障的影响。它通过维修和维护活动恢复到其初始和以前的恶化状态。维护和维修持续时间都被假定为指数随机变量。此外，假定制造产品的质量受系统当前劣化水平的影响。本文的目的是找到冲突性能指标之间的最佳权衡，以优化系统的总预期利润。为了解决此类优化问题，研究人员经常使用动态规划。不过这个方法，由于复杂性原因，不适用于所解决的问题。为此，提出了一种基于强化学习的方法，以获得最优的联合生产、维护和产品质量控制策略。据作者所知，所提出的方法是新颖的，学术文献中很少有这种实现的例子。