The Robust Redundancy Allocation Problem in Series-Parallel Systems With Budgeted Uncertainty

Feizollahi, Mohammad Javad; Ahmed, Shabbir; Modarres, Mohammad

doi:10.1109/tr.2014.2299191

Cited by 38 publications

(18 citation statements)

References 32 publications

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“…It should be mentioned that two redundancy strategies including active (A) and cold standby (C) are to be selected in this paper and the variables for the redundant strategy are restricted in the interval [1,2]. The round function is utilized to choose values from either 1 which means the subsystem follows the active strategy or 2 which means the cold standby strategy is employed.…”

Section: Solution Representationmentioning

confidence: 99%

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Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Kong

Gao

Ouyang

et al. 2015

Reliability Engineering & System Safety

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Section: Solution Representationmentioning

confidence: 99%

“…The system reliability is maximized through adding appropriate redundant components to each subsystem while satisfying certain system-level constraints such as cost and weight. Since various real-world applications can be modeled as the well-known RAP, many researchers have studied such an arduous NP-hard problem [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Kong

Gao

Ouyang

et al. 2015

Reliability Engineering & System Safety

View full text Add to dashboard Cite

“…In practice, for an uncertain mixed integer programming (MIP) problem with interval data, solving robust counterparts for budgeted uncertainty sets is much easier than finding the minmax regret solution. For example, in redundancy allocation problems, this difference is obvious by comparing the results in [48][49][50][51], respectively. In addition, former method can find solutions with different levels of conservativeness, while the latter approach outputs only one conservative solution.…”

Section: Introductionmentioning

confidence: 99%

Robust quadratic assignment problem with budgeted uncertain flows

Feizollahi

Feyzollahi

2015

Operations Research Perspectives

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h i g h l i g h t s• A robust approach for quadratic assignment problem (RQAP) with budgeted uncertainty. • An exact and two heuristic methods to solve RQAP.• Extensive experiments to show performance of methods and quality of solutions.• RQAP can be solved significantly faster than minmax regret QAP. • RQAP has adjustable conservativeness while minmax regret QAP has not. a b s t r a c tWe consider a generalization of the classical quadratic assignment problem, where material flows between facilities are uncertain, and belong to a budgeted uncertainty set. The objective is to find a robust solution under all possible scenarios in the given uncertainty set. We present an exact quadratic formulation as a robust counterpart and develop an equivalent mixed integer programming model for it. To solve the proposed model for large-scale instances, we also develop two different heuristics based on 2-Opt local search and tabu search algorithms. We discuss performance of these methods and the quality of robust solutions through extensive computational experiments.

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“…They implemented robust deviation called minimax regret to address uncertainty of component reliability. Feizollahi et al [31] extended the constrained redundancy optimization problem such that component reliabilities belong to a uncertainty set. In our new model, RAP is extended by relating uncertain stress variables to component reliability with different system perspectives, including regret or opportunity loss analysis.…”

Section: Introductionmentioning

confidence: 99%

System redundancy optimization with uncertain stress-based component reliability: Minimization of regret

Chatwattanasiri

Coit

Wattanapongsakorn

2016

Reliability Engineering & System Safety

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a b s t r a c tSystem reliability design optimization models have been developed for systems exposed to changing and diverse stress and usage conditions. Uncertainty is addressed through defining a future operating environment where component stresses have shifted or changed for different future usage scenarios. Due to unplanned variations or changing environments and operating stresses, component and system reliability often cannot be predicted or estimated without uncertainty. Component reliability can vary due to a relative increase/decrease of stresses or operating conditions. The uncertain parameters of stresses have been incorporated directly into the new decision-making model. Risk analysis perspectives, including risk-neutral and risk-averse, are considered as system reliability objective functions. A regret function is defined, and minimization of the maximum regret provides an objective function based on random future usage stresses. This is an entirely new formulation of the redundancy allocation problem, but it is a relevant one for some problem domains. The redundancy allocation problem is solved to select the best design solution when there are multiple choices of components and system-level constraints. Nonlinear programming and a neighborhood search heuristic method are recommended to obtain the integer solutions for risk-based formulations.

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The Robust Redundancy Allocation Problem in Series-Parallel Systems With Budgeted Uncertainty

Cited by 38 publications

References 32 publications

Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Robust quadratic assignment problem with budgeted uncertain flows

System redundancy optimization with uncertain stress-based component reliability: Minimization of regret

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