Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran.
ISA Trans. 2014 Jan;53(1):17-24. doi: 10.1016/j.isatra.2013.08.002. Epub 2013 Aug 30.
Traditionally, in the redundancy allocation problem (RAP), two general classes of optimization problems are considered; reliability optimization and availability optimization. Contrary to reliability optimization, fewer researchers have studied availability optimization to find out the optimal combination of components type and redundancy levels for each subsystem in a system for maximizing (or minimizing) the objectives. In each problem it is assumed that either the entire components are repairable or they are non-repairable. However, in real world situations, systems usually consist of both repairable and non-repairable components. In this paper a new Mixed Integer Nonlinear Programming (MINLP) model is presented to analyze the availability optimization of a system with a given structure, using both repairable and non-repairable components, simultaneously. To find the solution of the introduced MINLP, an efficient Genetic Algorithm (GA) is also developed. Furthermore, to show the efficiency of the proposed GA, a numerical example is presented. Experimental results demonstrate that the proposed GA has a better performance compared to one of the most recommended algorithm in the literature.
传统上，在冗余分配问题 (RAP) 中，考虑了两类优化问题；可靠性优化和可用性优化。与可靠性优化不同，很少有研究人员研究可用性优化，以找出系统中每个子系统的最佳组件类型和冗余级别组合，以最大化（或最小化）目标。在每个问题中，假设要么所有组件都是可修复的，要么它们是不可修复的。然而，在现实世界的情况下，系统通常由可修复和不可修复的组件组成。在本文中，提出了一种新的混合整数非线性规划 (MINLP) 模型，用于同时使用可修复和不可修复的组件来分析具有给定结构的系统的可用性优化。为了找到引入的 MINLP 的解决方案，还开发了一种有效的遗传算法 (GA)。此外，为了展示所提出的 GA 的效率，提出了一个数值示例。实验结果表明，与文献中最推荐的算法之一相比，所提出的 GA 具有更好的性能。
