Reliability stochastic optimization for a series system with interval component reliability via genetic algorithm

被引:60
作者
Bhunia, A. K. [1 ]
Sahoo, L. [2 ]
Roy, D. [3 ]
机构
[1] Univ Burdwan, Dept Math, Burdwan 713104, W Bengal, India
[2] Raniganj Girls Coll, Dept Math, Raniganj 713347, W Bengal, India
[3] Univ Burdwan, Ctr Management Studies, Burdwan 713104, W Bengal, India
关键词
Genetic algorithm; Interval numbers; Reliability optimization; Chance constraints; Penalty technique; Redundancy; REDUNDANCY-OPTIMIZATION;
D O I
10.1016/j.amc.2010.01.106
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This paper deals with chance constraints based reliability stochastic optimization problem in the series system. This problem can be formulated as a nonlinear integer programming problem of maximizing the overall system reliability under chance constraints due to resources. The assumption of traditional reliability optimization problem is that the reliability of a component is known as a fixed quantity which lies in the open interval (0, 1). However, in real life situations, the reliability of an individual component may vary due to some realistic factors and it is sensible to treat this as a positive imprecise number and this imprecise number is represented by an interval valued number. In this work, we have formulated the reliability optimization problem as a chance constraints based reliability stochastic optimization problem with interval valued reliabilities of components. Then, the chance constraints of the problem are converted into the equivalent deterministic form. The transformed problem has been formulated as an unconstrained integer programming problem with interval coefficients by Big-M penalty technique. Then to solve this problem, we have developed a real coded genetic algorithm (GA) for integer variables with tournament selection, uniform crossover and one-neighborhood mutation. To illustrate the model two numerical examples have been solved by our developed GA. Finally to study the stability of our developed GA with respect to the different GA parameters, sensitivity analyses have been done graphically. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码:929 / 939
页数:11
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