The Robust Deviation Redundancy Allocation Problem With Interval Component Reliabilities

Feizollahi, Mohammad Javad; Modarres, Mohammad

doi:10.1109/tr.2012.2221032

Cited by 41 publications

(16 citation statements)

References 22 publications

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“…In Hussain and Murthy (2003), the uncertain component failure rate has been modeled by a reliability growth model, and an optimization model has been proposed to find the best trade-off between the development cost and the warranty cost. Feizollahi and Modarres (2012) proposed a reliability design framework to address the uncertain component failure rates by using the Min-Max regret (also known as robust deviation) approach and transformed the nonlinear programming formulation to a linear binary version to get the exact solutions. None of the above works considered interval availability.…”

Section: Literaturementioning

confidence: 99%

Reliability optimization for series systems under uncertain component failure rates in the design phase

Peng

Houtum

et al. 2018

International Journal of Production Economics

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. (2018). Reliability optimization for series systems under uncertain component failure rates in the design phase. International Journal of Production Economics, 196(2), 163-175. DOI: 10.1016163-175. DOI: 10. /j.ijpe.2017 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. AbstractWe develop an optimization model to determine the reliability design of critical components in a serial system. The system is under a service contract, and a penalty cost has to be paid by the OEM when the total system down time exceeds a predetermined level, which complicates the evaluation of the expected cost under a given reliability design. Furthermore, in the design phase for each critical component, all possible designs are subject to uncertain component failure rates. Considering the computational intractability of evaluating the system performance, we develop approximate evaluation methods which take the system uncertainty into account. Numerical results show that the method which includes randomness in the number of failures, failure rates and repair times leads to efficient and accurate evaluations and to close-to-optimal design decisions when used in an enumeration procedure for the optimization problem. We also show that ignoring these three types of uncertainty may result in bad design decisions.

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Section: Literaturementioning

confidence: 99%

Reliability optimization for series systems under uncertain component failure rates in the design phase

Peng

Houtum

et al. 2018

International Journal of Production Economics

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“…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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“…Several other papers proposed the constrained RAP with interval valued reliability of each component [19][20][21][22]. Although several approaches have been proposed for system reliability optimization considering uncertainty, these approaches have not considered uncertain stress directly as it relates to component and system reliability and risk.…”

Section: Introductionmentioning

confidence: 99%

“…For system reliability evaluation, Feizollahi et al [22] proposed the linear-transformed optimization model with constraints. They implemented robust deviation called minimax regret to address uncertainty of component reliability.…”

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 Deviation Redundancy Allocation Problem With Interval Component Reliabilities

Cited by 41 publications

References 22 publications

Reliability optimization for series systems under uncertain component failure rates in the design phase

Reliability optimization for series systems under uncertain component failure rates in the design phase

Robust quadratic assignment problem with budgeted uncertain flows

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

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