Practical solutions for multi-objective optimization: An application to system reliability design problems

Taboada, Heidi A.; Baheranwala, Fatema; Coit, David W.; Wattanapongsakorn, Naruemon

doi:10.1016/j.ress.2006.04.014

Cited by 138 publications

(61 citation statements)

References 17 publications

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“…Figure 2, for example, depicts the Pareto-optimal frontier for the multiobjective optimization problem in Equation 6, with the instantaneous tradeoff among • and • illustrated for a particular decision-maker-defined optimal decision u*. Examples of reliability-based multiobjective decision-making from a Pareto-optimal frontier include those by Huang, Tian, and Zuo (2005) ;Certa, Galante, Lupo, and Passananti (2011);and Taboada, Baheranwala, Coit, and Wattanapongsakorn (2007).…”

Section: Uncertainty Sensitivity Index Methodsmentioning

confidence: 99%

Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

Barker¹

2013

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The aim of this research work is to develop a framework for making coordinated acquisitions decisions, integrating: (i) schedules of maintenance based on the condition of system components, and (ii) trigger acquisition of multiple vendor maintenance, repair, and overhaul (MRO) supplies and services. To do so, we develop a multi-objective optimization maintenance/acquisition scheduling algorithm where tradeoffs are made between two competing objectives: maximizing conditionbased component reliability while maximizing the sensitivity of condition-based reliability to changes in the component's condition (e.g., degradation). This scheduling algorithm integrates with an acquisition algorithm to addresses vendor lead-time. Case studies broadly inspired by Tinker Air Force Base, the largest Air Force MRO hub in the US, in Oklahoma City, OK, illustrate the framework. Keywords

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Section: Uncertainty Sensitivity Index Methodsmentioning

confidence: 99%

Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

Barker¹

2013

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“…[153], [155], SOO(9) [17], [28], [68], [116], [193], [194], [211], [212], [240] COST(1) [72], GENERAL(1) [194], MAPPING(1) [153], MEMORY(1) [153], NOT PRESENTED(1) [206], PATH LOSS(1) [237], PERFORMANCE(8) [17], [28], [67], [68], [116], [193], [211], [212], PHYSICAL(3) [155], [237], [240], REDUNDANCY LEVEL(1) [165], STRUCTURAL(2) [84], [188], TIMING(1) [240] Weight (5) ES(4) [137], [215]- [217], GENERAL(1) [147] DT(5) [137], [147], [215]- [217] AVAILABILITY(1) [217], COST(5) [137], [147], [215]- [217], RELIABILITY(4) [137], [147], …”

Section: Mts(2)mentioning

confidence: 99%

Software Architecture Optimization Methods: A Systematic Literature Review

Aleti

Bühnová

Grunske

et al. 2013

IIEEE Trans. Software Eng.

243

146

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Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Abstract-Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Furthermore, the systematic analysis of the research literature provided in this review aims to help the research community in consolidating the existing research efforts and deriving a research agenda for future developments.

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“…The use of multiple objective evolutionary algorithms (MOEAs) in robust design of systems has been reported by few publications found in literature (Konak et al 2006;Salazar and Rocco 2007;Taboada et al 2007). Most of the referred approaches are based on dominance concepts to build the Pareto front proposed by Deb (2001).…”

Section: Multi-objective Evolutionary Algorithmmentioning

confidence: 99%

Optimal design of composite shells based on minimum weight and maximum feasibility robustness

Curnis

Hoffbauer

2015

Int J Mech Mater Des

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A robust design optimization (RDO) approach for minimum weight and safe shell composite structures with minimal variability into design constraints under uncertainties is proposed. A new concept of feasibility robustness associated to the variability of design constraints is considered. So, the feasibility robustness is defined through the determinant of variance-covariance matrix of constraint functions introducing in this way the joint effects of the uncertainty propagations on structural response. A new framework considering aleatory uncertainty into RDO of composite structures is proposed. So, three classes of variables and parameters are identified: deterministic design variables, random design variables and random parameters. The bi-objective optimization search is performed using on a new approach based on two levels of dominance denoted by CoDominance-based Genetic Algorithm (CoDGA). The use of evolutionary concepts together sensitivity analysis based on adjoint variable method is a new proposal. The examples with different sources of uncertainty show that the Pareto front definition depends on random design variables and/or random parameters considered in RDO. Furthermore, the importance to control the uncertainties on the feasibility of constraints is demonstrated. CoDGA approach is a powerfully tool to help designers to make decision establishing the priorities between performance and robustness.

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Practical solutions for multi-objective optimization: An application to system reliability design problems

Cited by 138 publications

References 17 publications

Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

Software Architecture Optimization Methods: A Systematic Literature Review

Optimal design of composite shells based on minimum weight and maximum feasibility robustness

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