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

Bhunia, Asoke Kumar; Sahoo, Laxminarayan; Roy, Dilip

doi:10.1016/j.amc.2010.01.106

Cited by 62 publications

(33 citation statements)

References 12 publications

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“…They solved these problems by applying GA and the concept of Pareto optimality. Taguchi et al [24] considered the reliability optimization problem as a nonlinear programming with interval coefficients in the objective and solved the resulted problem by GA. Taguchi et al [25] transformed an optimal design of system reliability problem with interval coefficients into the single objective nonlinear integer programming problem without interval coefficients and solved it by an improved GA. Bhunia et al [26] considered a reliability optimization problem in an n-stage series system with interval valued reliabilities and stochastic resource constraint with known probability distributions such as uniform, normal and log normal distributions. After converting the problem into its equivalent deterministic form, they employed the GA method to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Robust counterpart optimization for the redundancy allocation problem in series-parallel systems with component mixing under uncertainty

Soltani

Safari

Sadjadi

2015

Applied Mathematics and Computation

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Section: Introductionmentioning

confidence: 99%

Robust counterpart optimization for the redundancy allocation problem in series-parallel systems with component mixing under uncertainty

Soltani

Safari

Sadjadi

2015

Applied Mathematics and Computation

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“…During the past, several techniques [20] have been proposed to handle the constraints in genetic algorithms for solving the optimization problem. Recently, Gupta et al [6] and Bhunia et al [1] solved the optimization problem using Big-M penalty method. In this method, the given constrained optimization problem is converted into an unconstrained optimization problem by penalizing a large positive number say, M and called this penalty as Big-M penalty.…”

Section: Big-m Penalty Techniquementioning

confidence: 99%

“…In this connection, the works of Gupta et. [6], Bhunia et al [1], Sahoo et al [27], Bhunia and Sahoo [2], Sahoo et al [26], Sahoo et al [28], Mahato et al [19], Sahoo et al [29], [30] are worth mentioning.…”

Section: Introductionmentioning

confidence: 99%

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Maximizing System Reliability of a Four Stage RAP with Two Chance Constraints Having Stochastic Reliability Components

2016

IJSR

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This paper deals with the solution of the chance constrained reliability-redundancy allocation problems in imprecise environment. The reliabilities of the components are imprecise numbers and the type of the constraints are chance constraints i.e., stochastic in nature. This paper proposes a stochastic simulation based genetic algorithm approach for solving the reliability optimization problems of the type mentioned. The impreciseness is represented in the stochastic approach. In case of stochastic approach, the reliabilities of the components are taken to be random variables having normal distribution. Then Monte Carlo simulation technique is applied to transform the chance constraints into the deterministic ones. Then Big-M penalty technique is applied to transform the problem to unconstrained one. The altered problem is then solved by the real coded genetic algorithm based on stochastic simulation. Some numerical illustrations are presented to show the performance of the proposed procedure.

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“…In this work, one-neighbourhood mutation has been used. The details of oneneighborhood mutation is available in Bhunia et al (2010).…”

Section: Crossover and Mutationmentioning

confidence: 99%

An application of extended elitist non-dominated sorting Genetic Algorithm in multi-objective linear programming problem of tea industry with interval objectives

Bhunia¹,

Biswas²,

Sen³

2014

10.5267/j.uscm

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In this paper, we have modeled a decision making problem of a tea industry as a multi-objective optimization problem in interval environment. The goal of this problem is to maximize the overall profit as well as to minimize the total production cost subject to the given resource constraints depending on budget, storage space and allotted processing times in different machines. For this purpose, the problem has been formulated as a multi-objective integer linear programming problem with interval objectives. To solve the problem, we have proposed extended elitist non-dominated sorting genetic algorithm (ENSGA-II) for integer variables with interval fitness, crowded tournament selection, intermediate crossover, one neighborhood mutation and elitism. To develop this algorithm, we have proposed modified non-dominated sorting and crowding distance based on interval mathematics and interval order relations. Finally, to test the performance of the proposed algorithm, a numerical example has been solved.

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

Cited by 62 publications

References 12 publications

Robust counterpart optimization for the redundancy allocation problem in series-parallel systems with component mixing under uncertainty

Robust counterpart optimization for the redundancy allocation problem in series-parallel systems with component mixing under uncertainty

Maximizing System Reliability of a Four Stage RAP with Two Chance Constraints Having Stochastic Reliability Components

An application of extended elitist non-dominated sorting Genetic Algorithm in multi-objective linear programming problem of tea industry with interval objectives

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