Multiobjective engineering design optimization problems: a sensitivity analysis approach

This paper proposes two new approaches for the sensitivity analysis of multiobjective design optimization problems whose performance functions are highly susceptible to small variations in the design variables and/or design environment parameters. In both methods, the less sensitive design alternati...

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Main Authors: Oscar Brito Augusto, Fouad Bennis, Stephane Caro
Format: Article
Language:English
Published: Sociedade Brasileira de Pesquisa Operacional 2012-12-01
Series:Pesquisa Operacional
Subjects:
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382012000300006&lng=en&tlng=en
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spelling doaj-816da28a28674506be94426c6d6d20a82020-11-24T23:09:58ZengSociedade Brasileira de Pesquisa OperacionalPesquisa Operacional1678-51422012-12-0132357559610.1590/S0101-74382012000300006S0101-74382012000300006Multiobjective engineering design optimization problems: a sensitivity analysis approachOscar Brito Augusto0Fouad Bennis1Stephane Caro2Universidade de São PauloCommunications et Cybernétique de NantesCommunications et Cybernétique de NantesThis paper proposes two new approaches for the sensitivity analysis of multiobjective design optimization problems whose performance functions are highly susceptible to small variations in the design variables and/or design environment parameters. In both methods, the less sensitive design alternatives are preferred over others during the multiobjective optimization process. While taking the first approach, the designer chooses the design variable and/or parameter that causes uncertainties. The designer then associates a robustness index with each design alternative and adds each index as an objective function in the optimization problem. For the second approach, the designer must know, a priori, the interval of variation in the design variables or in the design environment parameters, because the designer will be accepting the interval of variation in the objective functions. The second method does not require any law of probability distribution of uncontrollable variations. Finally, the authors give two illustrative examples to highlight the contributions of the paper.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382012000300006&lng=en&tlng=enmultiobjective optimizationPareto-optimal solutionssensitivity analysis
collection DOAJ
language English
format Article
sources DOAJ
author Oscar Brito Augusto
Fouad Bennis
Stephane Caro
spellingShingle Oscar Brito Augusto
Fouad Bennis
Stephane Caro
Multiobjective engineering design optimization problems: a sensitivity analysis approach
Pesquisa Operacional
multiobjective optimization
Pareto-optimal solutions
sensitivity analysis
author_facet Oscar Brito Augusto
Fouad Bennis
Stephane Caro
author_sort Oscar Brito Augusto
title Multiobjective engineering design optimization problems: a sensitivity analysis approach
title_short Multiobjective engineering design optimization problems: a sensitivity analysis approach
title_full Multiobjective engineering design optimization problems: a sensitivity analysis approach
title_fullStr Multiobjective engineering design optimization problems: a sensitivity analysis approach
title_full_unstemmed Multiobjective engineering design optimization problems: a sensitivity analysis approach
title_sort multiobjective engineering design optimization problems: a sensitivity analysis approach
publisher Sociedade Brasileira de Pesquisa Operacional
series Pesquisa Operacional
issn 1678-5142
publishDate 2012-12-01
description This paper proposes two new approaches for the sensitivity analysis of multiobjective design optimization problems whose performance functions are highly susceptible to small variations in the design variables and/or design environment parameters. In both methods, the less sensitive design alternatives are preferred over others during the multiobjective optimization process. While taking the first approach, the designer chooses the design variable and/or parameter that causes uncertainties. The designer then associates a robustness index with each design alternative and adds each index as an objective function in the optimization problem. For the second approach, the designer must know, a priori, the interval of variation in the design variables or in the design environment parameters, because the designer will be accepting the interval of variation in the objective functions. The second method does not require any law of probability distribution of uncontrollable variations. Finally, the authors give two illustrative examples to highlight the contributions of the paper.
topic multiobjective optimization
Pareto-optimal solutions
sensitivity analysis
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382012000300006&lng=en&tlng=en
work_keys_str_mv AT oscarbritoaugusto multiobjectiveengineeringdesignoptimizationproblemsasensitivityanalysisapproach
AT fouadbennis multiobjectiveengineeringdesignoptimizationproblemsasensitivityanalysisapproach
AT stephanecaro multiobjectiveengineeringdesignoptimizationproblemsasensitivityanalysisapproach
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