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Unit degradation complicates the comprehensive optimization of reliability design and preventive maintenance (PM) policies for largescale systems considering the life-cycle. Based on the unit failure rate obeying the Weibull distribution, we propose a cost optimization model for large-scale systems under reliability constraints from the life-cycle perspective. In this study, we consider a simple multi-unit preventive joint maintenance policy where units are assessed and fixed only during planned inspections. However, nonlinear optimization becomes increasingly difficult due to the exponential combination growth caused by numerous units. To overcome this challenge, a genetic algorithm (GA) program is adopted to obtain the global optimal solution covering the unit reliability in the design and manufacturing stages and system PM period in the operation stage. Through real-world example analysis, the correctness and effectiveness of the proposed model and algorithm are verified. The relationship among decision variables, such as maintenance improvement factor, unit reliability, and PM period, is examined. The results simplify the reliability design process for system engineers and enrich reliability theory and applications. [ABSTRACT FROM AUTHOR]

The multi-state weighted k-out-of-n system has been widely studied in recent research. In this paper, two improved availability evaluation methods of an extended multi-state weighted k-out-of-n system with hierarchical performance requirements are presented. Such a system is capable of featuring multiple performance requirements at different system hierarchical levels that are not independent of each other, which is more general than the traditional multi-state weighted k-out-of-n system model. To solve the problem of inter-level performance dependencies, a new method combining the universal generating function (UGF) with hierarchical operators is developed to evaluate the system availability. Moreover, a simple recursive algorithm is proposed to reduce the computational complexity to access the lower and upper bounds of availability. Finally, numerical examples and analysis are provided to validate the proposed methods. The results show that the proposed methods are more general and flexible, and they can provide references to designers for the performance improvement of complex systems. [ABSTRACT FROM AUTHOR]