2012
DOI: 10.1016/j.cie.2012.02.004
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A bi-objective model to optimize reliability and cost of system with a choice of redundancy strategies

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Cited by 112 publications
(61 citation statements)
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“…They proposed non-dominated sorting genetic algorithms (NSGA-II) and multi-objective particle swarm optimization (MOPSO) to solve the problem. The proposed problem of this paper is similar with the model originally developed by Chambari et al (2012), except that we use different techniques to compare the results.…”
Section: Literature Reviewmentioning
confidence: 86%
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“…They proposed non-dominated sorting genetic algorithms (NSGA-II) and multi-objective particle swarm optimization (MOPSO) to solve the problem. The proposed problem of this paper is similar with the model originally developed by Chambari et al (2012), except that we use different techniques to compare the results.…”
Section: Literature Reviewmentioning
confidence: 86%
“…As stated in the introduction section, there are two objective functions similar to Chambari et al (2012) where the first one is associated with reliability and the second one is related to the cost. There are also two types of constrains including weight and volume.…”
Section: The Proposed Model and Its Assumptions Principlesmentioning
confidence: 99%
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“…In the cases where the number of agents and their levels is high, Taguchi method is more e cient than complete factorial method. For orthogonal array, L27 equaling 27 is much less than the number required for complete factorial method [33,29]. In order to tune the parameters, the Mean Ideal Distance (MID) is selected as the main response in Taguchi analysis.…”
Section: Parameter Tuningmentioning
confidence: 99%
“…Chambari et al [11] solved an M/M/1/k queue model by using both NSGA-II and nondominated ranking genetic algorithms (NRGA). Chambari et al [12] implemented NSGA-II to optimize cost and reliability of the whole system in a redundancy allocation problem. Mehdizadeh and Tavakkoli-Moghaddam [32] proposed a new meta-heuristic optimization algorithm, namely vibration damping optimization (VDO) to solve the parallel machine scheduling problem; VDO is based on the concept of vibration damping in mechanical vibration.…”
Section: Introductionmentioning
confidence: 99%