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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Sociedade Brasileira de Pesquisa Operacional
2012-12-01
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Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382012000300006 |
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doaj-9e5df552aebd45409050153570967f832020-11-24T23:12:23ZengSociedade Brasileira de Pesquisa OperacionalPesquisa Operacional0101-74381678-51422012-12-01323575596Multiobjective engineering design optimization problems: a sensitivity analysis approachOscar Brito AugustoFouad BennisStephane CaroThis 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=S0101-74382012000300006multiobjective 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 |
0101-7438 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 |
work_keys_str_mv |
AT oscarbritoaugusto multiobjectiveengineeringdesignoptimizationproblemsasensitivityanalysisapproach AT fouadbennis multiobjectiveengineeringdesignoptimizationproblemsasensitivityanalysisapproach AT stephanecaro multiobjectiveengineeringdesignoptimizationproblemsasensitivityanalysisapproach |
_version_ |
1725601059612655616 |