The present study proposes a novel simulation-based optimization model for a series-parallel redundancy allocation problem (RAP) under heterogeneous components for reliability maximization subjected to system-level constraints through the identification of the optimal redundancy strategy, component type, and subsystem component count. To obtain higher practicality, active, cold-standby, mixed, and K-mixed redundancy strategies are incorporated as the decision variables. In general, as it is difficult to determine a closed-form (excluding active redundancy strategies), it is not possible to analytically assess system reliability. Earlier studies on system reliability optimization applied convenient lower bound approximation. This limits higher reliability levels. In order to tackle this problem, this study adopted simulation sampling to make unbiased efficient reliability estimates. To this end, 4Dscript interpreting programming was utilized to develop a simulation model. As RAP has a combinatorial nature and is a random and NP-hard problem, this study adopted the genetic algorithm (GA) optimization. To validate the model and assess GA efficiency, the numerical findings of some benchmark tests were employed. The proposed approach outperformed earlier approaches in reliability and confidence.

本研究提出了一种新颖的基于仿真的优化模型，用于通过识别最优冗余策略，组件类型和子系统组件数，在异构组件下实现系统级约束下的可靠性最大化的串联并联冗余分配问题（RAP） 。为了获得更高的实用性，将主动，冷备用，混合和K混合冗余策略作为决策变量。通常，由于很难确定封闭形式（不包括主动冗余策略），因此无法分析评估系统可靠性。早期的系统可靠性优化研究采用了便捷的下界近似。这限制了更高的可靠性级别。为了解决这个问题，这项研究采用模拟抽样来进行无偏的有效可靠性估算。为此，利用4Dscript解释程序来开发仿真模型。由于RAP具有组合性质，并且是一个随机且具有NP困难的问题，因此本研究采用了遗传算法（GA）优化。为了验证模型并评估GA效率，采用了一些基准测试的数值结果。所提出的方法在可靠性和置信度方面优于早期方法。使用一些基准测试的数值结果。所提出的方法在可靠性和置信度方面优于早期方法。使用一些基准测试的数值结果。所提出的方法在可靠性和置信度方面优于早期方法。