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Selective maintenance for multi-state series-parallel systems under economic dependence

机译：经济依赖性下的多状态串并联系统的选择性维护

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摘要

This paper presents a study on selective maintenance for multi-state series-parallel systems with economically dependent components. In the selective maintenance problem, the maintenance manager has to decide which components should receive maintenance activities within a finite break between missions. All the system reliabilities in the next operating mission, the available budget and the maintenance time for each component from its current state to a higher state are taken into account in the optimization models. In addition, the components in series-parallel systems are considered to be economically dependent. Time and cost savings will be achieved when several components are simultaneously repaired in a selective maintenance strategy. As the number of repaired components increases, the saved time and cost will also increase due to the share of setting up between components and another additional reduction amount resulting from the repair of multiple identical components. Different optimization models are derived to find the best maintenance strategy for multi-state series-parallel systems. A genetic algorithm is used to solve the optimization models. The decision makers may select different components to be repaired to different working states based on the maintenance objective, resource availabilities and how dependent the repair time and cost of each component are.
机译：本文提出了一种具有经济依赖性组件的多状态串并联系统的选择性维护研究。在选择性维护问题中，维护经理必须决定在两次任务之间的有限间隔内应接受维护的组件。优化模型考虑了下一个运行任务中所有系统的可靠性，每个组件从其当前状态到更高状态的可用预算和维护时间。另外，串并联系统中的组件被认为在经济上是依赖的。当以选择性维护策略同时修复多个组件时，将节省时间和成本。随着维修部件数量的增加，节省的时间和成本也将增加，这是由于部件之间的安装份额以及由于维修多个相同部件而导致的另一个额外减少量。得出了不同的优化模型，以找到多状态串并联系统的最佳维护策略。遗传算法用于求解优化模型。决策者可以根据维护目标，资源可用性以及每个组件的维修时间和成本的依赖性来选择要维修到不同工作状态的不同组件。

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