Abstract In highly flexible and integrated manufacturing systems, such as semiconductor fabs, strong interactions between the equipment condition, operations executed on the various machines and the outgoing product quality necessitate integrated decision making in the domains of maintenance scheduling and production operations. Furthermore, in highly complex manufacturing equipment, the underlying condition is not directly observable and can only be inferred probabilistically from the available sensor readings. In order to deal with interactions between maintenance and production operations in Flexible Manufacturing Systems (FMSs) in which equipment conditions are not perfectly observable, we propose in this paper a decision-making method based on a Partially Observable Markov Decision Processes (POMDP's), yielding an integrated policy in the realms of maintenance scheduling and production sequencing. Optimization was pursued using a metaheuristic method that used the results of discrete-event simulations of the underlying manufacturing system. The new approach is demonstrated in simulations of a generic semiconductor manufacturing cluster tool. The results showed that, regardless of uncertainties in the knowledge of actual equipment conditions, jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.

中文翻译：

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使用不完善的退化状态观察进行综合维护和运营决策

摘要 在高度灵活和集成的制造系统中，例如半导体工厂，设备状态、在各种机器上执行的操作和出厂产品质量之间的强相互作用需要在维护计划和生产操作领域进行综合决策。此外，在高度复杂的制造设备中，潜在条件无法直接观察到，只能从可用的传感器读数中进行概率推断。为了处理不能完全观察到设备状况的柔性制造系统 (FMS) 中维护和生产操作之间的相互作用，我们在本文中提出了一种基于部分可观察马尔可夫决策过程 (POMDP) 的决策方法，在维护计划和生产排序领域产生综合策略。使用元启发式方法进行优化，该方法使用底层制造系统的离散事件模拟结果。这种新方法在通用半导体制造集群工具的模拟中得到了证明。结果表明，无论实际设备条件的知识存在不确定性，联合做出维护和生产排序决策始终优于当前单独做出这些决策的做法。这种新方法在通用半导体制造集群工具的模拟中得到了证明。结果表明，无论实际设备条件的知识存在不确定性，联合做出维护和生产排序决策始终优于当前单独做出这些决策的做法。这种新方法在通用半导体制造集群工具的模拟中得到了证明。结果表明，无论实际设备条件的知识存在不确定性，联合做出维护和生产排序决策始终优于当前单独做出这些决策的做法。