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Multiobjective optimization of availability and cost in repairable systems design via genetic algorithms and discrete event simulation

机译：通过遗传算法和离散事件模拟对可修复系统设计中的可用性和成本进行多目标优化

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This paper attempts to provide a more realistic approach to the characterization of system reliability when handling redundancy allocation problems: it considers repairable series-parallel systems comprised of components subjected to corrective maintenance actions with failure-repair cycles modeled by renewal processes. A multiobjective optimization approach is applied since increasing the number of redundancies not only enlarges system reliability but also its associated costs. Then a multiobjective genetic algorithm is coupled with discrete event simulation and its solutions present the compromise between system reliability and cost. Two examples are provided. In the first one, the proposed algorithm is validated by comparison with results obtained from a system devised as to allow for analytical solutions of the objective functions. The second case analyzes a repairable system subjected to perfect repairs. Results from both examples show that the proposed method can be a valuable tool for the decision maker when choosing the system design.
机译：本文试图为处理冗余分配问题提供一种更可靠的系统可靠性特征描述方法：它考虑了可修复的串并联系统，该系统由经受纠正性维护措施的组件组成，并具有通过更新过程建模的故障维修周期。由于增加了冗余数量，不仅增加了系统可靠性，而且还增加了相关成本，因此采用了多目标优化方法。然后，将多目标遗传算法与离散事件仿真相结合，其解决方案提出了系统可靠性与成本之间的折衷方案。提供了两个示例。在第一个中，通过与从设计为允许目标函数的解析解的系统中获得的结果进行比较来验证所提出的算法。第二种情况分析了经过完美修复的可修复系统。两个示例的结果均表明，所提出的方法对于选择系统设计的决策者而言可能是有价值的工具。

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