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Joint optimization of lot sizing and condition-based maintenance for multi-component production systems

机译:多部件生产系统的批量优化和基于状态的维护的联合优化

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摘要

This paper presents a joint optimization model of production lot sizing and condition-based maintenance for a multi-component production system which produces the products to meet the demand in a finite time horizon. The components in the system deteriorate gradually with usage and age. To evaluate the conditions of components, inspections are carried out after each production run. The condition-based maintenance decision-making rules are based not only on the predictive reliability but also on the structural importance of each component. Moreover, the economic dependency among components is also considered when performing corrective maintenance actions. The preventive maintenance cost, the corrective maintenance cost, the setup cost, the inspection cost, the inventory holding cost and the shortage cost are considered in this paper. The aim is to minimize the total cost by jointly optimizing two decision variables: the production lot size and the preventive maintenance threshold. The optimal joint policy is obtained by coupling simulation model and genetic algorithm. Finally, the use and advantages of our model are illustrated through a case study of a cluster tool.
机译:本文提出了一种多批次生产系统的生产批次大小和基于状态的维护的联合优化模型,该系统可以在有限的时间范围内生产满足需求的产品。系统中的组件会随着使用和使用年限的增加而逐渐退化。为了评估组件的状况,在每次生产后都要进行检查。基于状态的维护决策规则不仅基于预测的可靠性,而且还基于每个组件的结构重要性。此外,在执行纠正性维护措施时,还应考虑组件之间的经济依赖性。本文考虑了预防性维护成本,纠正性维护成本,设置成本,检查成本,库存持有成本和短缺成本。目的是通过共同优化两个决策变量来最大程度地降低总成本:生产批次大小和预防性维护阈值。通过将仿真模型与遗传算法相结合,获得最优的联合策略。最后,通过对集群工具的案例研究来说明我们模型的用途和优势。

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