Surrogate Constraints Algorithm for Reliability Optimization Problems with Two Constraints

Nakagawa, Yuji; Miyazaki, Satoshi

doi:10.1109/tr.1981.5221024

Cited by 182 publications

(93 citation statements)

References 15 publications

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“…Fyffe et al [1] proposed a mathematical model for RAP as first research in this scope and they used dynamic programming to solve the model. Then, Nakagawa and Miyazaki [2] modified model presented in [1] via considering the upper limit of system weight between 159 and 191. They showed that using a surrogate constraints algorithm leads to solutions with higher reliability in comparison with dynamic programming through solving 33 problems.…”

Section: Introductionmentioning

confidence: 99%

Redundancy allocation problem with a mixed strategy for a system with k-out-of-n subsystems and time-dependent failure rates based on Weibull distribution: An optimization via simulation approach

et al. 2018

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Reliability improvement for electronics and mechanical systems is vital for engineers in order to design of these systems. For this reason, there are many researches in this scope to help engineers in real world applications. One of the useful methods in reliability optimization is redundancy allocation problem (RAP). In the most previous works, the failure rates of system components are considered to be constant based on negative exponential distribution; whereas, nearly all systems in real world have components with time-dependent failure rates; i.e., the failure rates of system components will be changed time by time. In this paper, we have worked on a RAP for a system under k-out-of-n subsystems with time-dependent components failure rates based on Weibull distribution. Also, the redundancy policy of the proposed system is considered as mixed strategy and the optimization method was based on the simulation technique to obtain reliability function as implicit function. Finally, a branch and bound algorithm has been used to solve the model, exactly.

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Section: Introductionmentioning

confidence: 99%

Redundancy allocation problem with a mixed strategy for a system with k-out-of-n subsystems and time-dependent failure rates based on Weibull distribution: An optimization via simulation approach

et al. 2018

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“…They solved this problem by using dynamic programming. Nakagawa and Miyazaki (1981) solved 33 problems by replacing an exact method and explained that, it would be a better idea to use alternative methods instead of dynamic programming. Bulfin and Liu (1985) discussed allocating redundant components subject to resource constraints to optimize some measure of system performance.…”

Section: Introductionmentioning

confidence: 99%

“…Onishi et al (2007) developed an exact solution to solve a series-parallel problem. As mentioned before, Nakagawa and Miyazaki (1981) solved 33 problems by replacing an exact method but their method could not find optimal solutions for three instances. Onishi et al (2007) developed an alternative method and could manage to solve all 33 problems, successfully.…”

Section: Introductionmentioning

confidence: 99%

Efficient optimization of multi-objective redundancy allocation problems in series-parallel systems

Arjestan¹

2017

10.5267/j.dsl

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“…Almost in all in the techniques available for reliability optimization, the assumption on uncertainty is based on precise probabilities and the reliabilities of system components are to be known and fixed positive numbers which lying in the interval [0, 1] and well discussed in [4], [8], [15], [16], [17], [22], [23], [24], [31], [32], [33]. The precise system reliability can be computed theoretically if both the above-mentioned conditions are satisfied.…”

Section: Introductionmentioning

confidence: 99%

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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Surrogate Constraints Algorithm for Reliability Optimization Problems with Two Constraints

Cited by 182 publications

References 15 publications

Redundancy allocation problem with a mixed strategy for a system with k-out-of-n subsystems and time-dependent failure rates based on Weibull distribution: An optimization via simulation approach

Redundancy allocation problem with a mixed strategy for a system with k-out-of-n subsystems and time-dependent failure rates based on Weibull distribution: An optimization via simulation approach

Efficient optimization of multi-objective redundancy allocation problems in series-parallel systems

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

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