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Optimal design of multi-state weighted k-out-of-n systems based on component design

机译:基于组件设计的多态加权k-out-n系统的优化设计

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This paper presents a study on design optimization of multi-state weighted k-out-of-n systems. The studied system reliability model is more general than the traditional k-out-of-n system model. The system and its components are capable of assuming a whole range of performance levels, varying from perfect functioning to complete failure. A utility value corresponding to each state is used to indicate the corresponding performance level. A widely studied reliability optimization problem is the "component selection problem", which involves selection of components with known reliability and cost characteristics. Less adequately addressed has been the problem of determining system cost and utility based on the relationships between component reliability, cost and utility. This paper addresses this topic. All the optimization problems dealt with in this paper can be categorized as either minimizing the expected total system cost subject to system reliability requirements, or maximizing system reliability subject to total system cost limitation. The resulting optimization problems are too complicated to be solved by traditional optimization approaches; therefore, genetic algorithm (GA) is used to solve them. Our results show that GA is a powerful tool for solving these kinds of problems.
机译:本文提出了一种多状态加权k-out-n系统的设计优化研究。所研究的系统可靠性模型比传统的k-of-n系统模型更为通用。该系统及其组件能够承担整个性能水平范围,从完善的功能到完全的故障不等。与每个状态相对应的效用值用于指示相应的性能水平。广泛研究的可靠性优化问题是“组件选择问题”,其中涉及选择具有已知可靠性和成本特征的组件。基于组件可靠性,成本和效用之间的关系确定系统成本和效用的问题尚未得到充分解决。本文解决了这个主题。本文中涉及的所有优化问题都可以归类为:根据系统可靠性要求将期望的总系统成本降至最低,或者将系统可靠性加以限制而使系统可靠性最大化。由此产生的优化问题太复杂了,无法用传统的优化方法解决。因此,使用遗传算法(GA)来解决它们。我们的结果表明,遗传算法是解决此类问题的有力工具。

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