Application of an iterative method and an evolutionary algorithm in fuzzy optimization

Detalhes bibliográficos
Autor(a) principal: Silva, Ricardo Coelho [UNIFESP]
Data de Publicação: 2012
Outros Autores: Cantão, Luiza A.p., Yamakami, Akebo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNIFESP
dARK ID: ark:/48912/0013000012txw
Texto Completo: http://dx.doi.org/10.1590/S0101-74382012005000018
http://repositorio.unifesp.br/handle/11600/7250
Resumo: This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain.
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spelling Application of an iterative method and an evolutionary algorithm in fuzzy optimizationfuzzy numberscut levelsfuzzy optimizationgenetic algorithmsThis work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain.Federal University of São Paulo Institute of Science and TechnologySão Paulo State University Environmental Engineering DepartmentUNICAMP School of Electrical and Computer Engineering Department of TelematicsUNIFESP, Institute of Science and TechnologySciELOSociedade Brasileira de Pesquisa OperacionalUniversidade Federal de São Paulo (UNIFESP)Universidade Estadual Paulista (UNESP)Universidade Estadual de Campinas (UNICAMP)Silva, Ricardo Coelho [UNIFESP]Cantão, Luiza A.p.Yamakami, Akebo2015-06-14T13:44:53Z2015-06-14T13:44:53Z2012-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion315-329application/pdfhttp://dx.doi.org/10.1590/S0101-74382012005000018Pesquisa Operacional. Sociedade Brasileira de Pesquisa Operacional, v. 32, n. 2, p. 315-329, 2012.10.1590/S0101-74382012005000018S0101-74382012000200004.pdf0101-7438S0101-74382012000200004http://repositorio.unifesp.br/handle/11600/7250ark:/48912/0013000012txwengPesquisa Operacionalinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UNIFESPinstname:Universidade Federal de São Paulo (UNIFESP)instacron:UNIFESP2024-08-05T05:15:24Zoai:repositorio.unifesp.br/:11600/7250Repositório InstitucionalPUBhttp://www.repositorio.unifesp.br/oai/requestbiblioteca.csp@unifesp.bropendoar:34652024-12-11T20:52:20.756334Repositório Institucional da UNIFESP - Universidade Federal de São Paulo (UNIFESP)false
dc.title.none.fl_str_mv Application of an iterative method and an evolutionary algorithm in fuzzy optimization
title Application of an iterative method and an evolutionary algorithm in fuzzy optimization
spellingShingle Application of an iterative method and an evolutionary algorithm in fuzzy optimization
Silva, Ricardo Coelho [UNIFESP]
fuzzy numbers
cut levels
fuzzy optimization
genetic algorithms
title_short Application of an iterative method and an evolutionary algorithm in fuzzy optimization
title_full Application of an iterative method and an evolutionary algorithm in fuzzy optimization
title_fullStr Application of an iterative method and an evolutionary algorithm in fuzzy optimization
title_full_unstemmed Application of an iterative method and an evolutionary algorithm in fuzzy optimization
title_sort Application of an iterative method and an evolutionary algorithm in fuzzy optimization
author Silva, Ricardo Coelho [UNIFESP]
author_facet Silva, Ricardo Coelho [UNIFESP]
Cantão, Luiza A.p.
Yamakami, Akebo
author_role author
author2 Cantão, Luiza A.p.
Yamakami, Akebo
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Federal de São Paulo (UNIFESP)
Universidade Estadual Paulista (UNESP)
Universidade Estadual de Campinas (UNICAMP)
dc.contributor.author.fl_str_mv Silva, Ricardo Coelho [UNIFESP]
Cantão, Luiza A.p.
Yamakami, Akebo
dc.subject.por.fl_str_mv fuzzy numbers
cut levels
fuzzy optimization
genetic algorithms
topic fuzzy numbers
cut levels
fuzzy optimization
genetic algorithms
description This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain.
publishDate 2012
dc.date.none.fl_str_mv 2012-08-01
2015-06-14T13:44:53Z
2015-06-14T13:44:53Z
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1590/S0101-74382012005000018
Pesquisa Operacional. Sociedade Brasileira de Pesquisa Operacional, v. 32, n. 2, p. 315-329, 2012.
10.1590/S0101-74382012005000018
S0101-74382012000200004.pdf
0101-7438
S0101-74382012000200004
http://repositorio.unifesp.br/handle/11600/7250
dc.identifier.dark.fl_str_mv ark:/48912/0013000012txw
url http://dx.doi.org/10.1590/S0101-74382012005000018
http://repositorio.unifesp.br/handle/11600/7250
identifier_str_mv Pesquisa Operacional. Sociedade Brasileira de Pesquisa Operacional, v. 32, n. 2, p. 315-329, 2012.
10.1590/S0101-74382012005000018
S0101-74382012000200004.pdf
0101-7438
S0101-74382012000200004
ark:/48912/0013000012txw
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Pesquisa Operacional
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 315-329
application/pdf
dc.publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
dc.source.none.fl_str_mv reponame:Repositório Institucional da UNIFESP
instname:Universidade Federal de São Paulo (UNIFESP)
instacron:UNIFESP
instname_str Universidade Federal de São Paulo (UNIFESP)
instacron_str UNIFESP
institution UNIFESP
reponame_str Repositório Institucional da UNIFESP
collection Repositório Institucional da UNIFESP
repository.name.fl_str_mv Repositório Institucional da UNIFESP - Universidade Federal de São Paulo (UNIFESP)
repository.mail.fl_str_mv biblioteca.csp@unifesp.br
_version_ 1818602558901452800

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