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Integrated optimization of condition-based preventive maintenance and production rescheduling with multi-phase processing speed selection and old machine scrap

机译:基于状态的预防性维护和生产重新调度的集成优化,具有多阶段处理速度选择和旧机器报废

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? 2023 Elsevier LtdWith the usage and aging of machine, condition-based preventive maintenance (CBPM) and old machine scrap are two common phenomena in the actual production, and the latter may lead to the original production-maintenance planning no longer available. Under this context, this paper addresses an integrated optimization problem of CBPM and production rescheduling with multi-phase processing speed selection and old machine scrap. More precisely, (1) a CBPM policy with sixteen inspection strategies and multi-phase processing speed selection is proposed to find some selectable maintenance plans for each machine; (2) a hybrid rescheduling strategy (HRS) is designed for responding to the dynamic event, and a rescheduling strategy is adaptively selected according to the average utilization rate (A1) of idle time of existing machines; and (3) an adaptive clustering-based bi-population co-evolutionary algorithm (ACBCA) is developed to solve the studied problem. In the numerical simulation, Taguchi method is first employed to find the optimal parameter setting for the proposed ACBCA. Second, the effect of predefined threshold of A1 is analyzed, and the optimal value is 0.3. Next, the superiority and competitiveness of the proposed ACBCA, CBPM policy and HRS are all demonstrated by comparing with other algorithms, CBPM policies and rescheduling strategies, respectively.
机译:?2023 Elsevier Ltd随着机器的使用和老化,基于状态的预防性维护(CBPM)和旧机器报废是实际生产中常见的两种现象,后者可能导致原有的生产维护计划不再可用。在此背景下,本文探讨了CBPM与生产重新调度的集成优化问题,并结合了多阶段处理速度选择和旧机报废。更准确地说,(1)提出了具有16种检查策略和多阶段处理速度选择的CBPM策略,为每台机器找到一些可选择的维护计划;(2)针对动态事件的响应,设计了混合调度策略(HRS),根据现有机器闲置时间的平均利用率(A1)自适应选择重调度策略;(3)针对所研究的问题,提出了一种基于自适应聚类的双种群协同进化算法(ACBCA)。在数值模拟中,首先采用田口方法为所提出的ACBCA找到最优参数设置。其次,分析了A1预定义阈值的影响,最优值为0.3;其次,通过与其他算法、CBPM政策和重新调度策略的对比,分别证明了所提出的ACBCA、CBPM政策和HRS的优越性和竞争力。

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