Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/336954
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampAndreani, Roberto-
dc.contributor.authorunicampSantos, Sandra Augusta-
dc.typeArtigopt_BR
dc.titleBilevel optimization with a multiobjective problem in the lower levelpt_BR
dc.contributor.authorAndreani, Roberto-
dc.contributor.authorRamirez, Viviana A.-
dc.contributor.authorSantos, Sandra A.-
dc.contributor.authorSecchin, Leonardo D.-
dc.subjectOtimização em dois níveis (Matemática)pt_BR
dc.subjectMétodos de restauração inexatapt_BR
dc.subjectExperimentos numéricospt_BR
dc.subject.otherlanguageBilevel optimization (Mathematics)pt_BR
dc.subject.otherlanguageInexact restoration methodspt_BR
dc.subject.otherlanguageNumerical experimentspt_BR
dc.description.abstractBilevel problems model instances with a hierarchical structure. Aiming at an efficient solution of a constrained multiobjective problem according with some pre-defined criterion, we reformulate this semivectorial bilevel optimization problem as a classic bilevel one. This reformulation intents to encompass all the objectives, so that the properly efficient solution set is recovered by means of a convenient weighted-sum scalarization approach. Inexact restoration strategies potentially take advantage of the structure of the problem under consideration, being employed as an alternative to the Karush-Kuhn-Tucker reformulation of the bilevel problem. Genuine multiobjective problems possess inequality constraints in their modeling, and these constraints generate theoretical and practical difficulties to our lower level problem. We handle these difficulties by means of a perturbation strategy, providing the convergence analysis, together with enlightening examples and illustrative numerical testspt_BR
dc.relation.ispartofNumerical Algorithmspt_BR
dc.publisher.cityNew York, NYpt_BR
dc.publisher.countryEstado Unidospt_BR
dc.publisherSpringerpt_BR
dc.date.issued2019-
dc.date.monthofcirculationJulypt_BR
dc.language.isoengpt_BR
dc.description.volume81pt_BR
dc.description.issuenumber3pt_BR
dc.description.lastpage915pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1017-1398pt_BR
dc.identifier.eissn1572-9265pt_BR
dc.identifier.doi10.1007/s11075-018-0576-1pt_BR
dc.identifier.urlhttps://link.springer.com/article/10.1007/s11075-018-0576-1pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsordocumentnumber303013/2013-3; 302915/2016-8pt_BR
dc.description.sponsordocumentnumber2013/05475-7; 2013/07375-0pt_BR
dc.date.available2020-03-19T20:53:23Z-
dc.date.accessioned2020-03-19T20:53:23Z-
dc.description.provenanceSubmitted by Cintia Oliveira de Moura (cintiaom@unicamp.br) on 2020-03-19T20:53:23Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-07-20T14:18:01Z : No. of bitstreams: 1 000471204900006.pdf: 1176348 bytes, checksum: 4ddb46452f51c1487a6d1af6812383d1 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-03-19T20:53:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/336954-
dc.contributor.departmentDepartamento de Matemática Aplicadapt_BR
dc.contributor.departmentDepartamento de Matemática Aplicadapt_BR
dc.contributor.unidadeInstituto de Matemática, Estatística e Computação Científicapt_BR
dc.subject.keywordMultiobjective optimizationpt_BR
dc.subject.keywordKKT reformulationpt_BR
dc.identifier.source471204900006pt_BR
dc.creator.orcid0000-0003-2031-4325pt_BR
dc.creator.orcid0000-0002-6250-0137pt_BR
dc.type.formArtigo originalpt_BR
dc.identifier.articleid946pt_BR
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