MILP-based clustering method for multi-objective optimization: Application to environmental problems

Autores
Oliva, Diego Gabriel; Guillén Gosálbez, G.; Mateo Sanz, J.m.; Jiménez Esteller, L.
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Multi-objective optimization (MOO) has recently emerged as a useful technique in environmental engineering. One major limitation of this approach is that its computational burden grows rapidly with the number of environmental objectives, which causes difficulties regarding the computation and visualization of the Pareto solutions. In this work we present several theoretical and algorithmic developments for grouping environmental objectives into clusters on the basis of which the multi-objective optimization can be performed, thereby facilitating the computation and analysis of the Pareto solutions. Our method is based on a novel mixed-integer linear program (MILP) that identifies in a systematic manner groups of objectives that behave similarly. We test the capabilities of our approach using several examples, in which we compare it against other well-known clustering methods.
Fil: Oliva, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina. Universitat Rovira I Virgili; España
Fil: Guillén Gosálbez, G.. Universitat Rovira I Virgili; España
Fil: Mateo Sanz, J.m.. Universitat Rovira I Virgili; España
Fil: Jiménez Esteller, L.. Universitat Rovira I Virgili; España
Materia
Environmental Objectives
Life Cycle Assessment
Multi-Objective Optimization
Pareto Solutions
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/6919
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling MILP-based clustering method for multi-objective optimization: Application to environmental problemsOliva, Diego GabrielGuillén Gosálbez, G.Mateo Sanz, J.m.Jiménez Esteller, L.Environmental ObjectivesLife Cycle AssessmentMulti-Objective OptimizationPareto Solutionshttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Multi-objective optimization (MOO) has recently emerged as a useful technique in environmental engineering. One major limitation of this approach is that its computational burden grows rapidly with the number of environmental objectives, which causes difficulties regarding the computation and visualization of the Pareto solutions. In this work we present several theoretical and algorithmic developments for grouping environmental objectives into clusters on the basis of which the multi-objective optimization can be performed, thereby facilitating the computation and analysis of the Pareto solutions. Our method is based on a novel mixed-integer linear program (MILP) that identifies in a systematic manner groups of objectives that behave similarly. We test the capabilities of our approach using several examples, in which we compare it against other well-known clustering methods.Fil: Oliva, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina. Universitat Rovira I Virgili; EspañaFil: Guillén Gosálbez, G.. Universitat Rovira I Virgili; EspañaFil: Mateo Sanz, J.m.. Universitat Rovira I Virgili; EspañaFil: Jiménez Esteller, L.. Universitat Rovira I Virgili; EspañaElsevier2013-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/6919Oliva, Diego Gabriel; Guillén Gosálbez, G.; Mateo Sanz, J.m.; Jiménez Esteller, L.; MILP-based clustering method for multi-objective optimization: Application to environmental problems; Elsevier; Computers and Chemical Engineering; 56; 9-2013; 202-2170098-1354enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2013.05.016info:eu-repo/semantics/altIdentifier/url/10.1016/j.compchemeng.2013.05.016info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:07:47Zoai:ri.conicet.gov.ar:11336/6919instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:07:47.379CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv MILP-based clustering method for multi-objective optimization: Application to environmental problems
title MILP-based clustering method for multi-objective optimization: Application to environmental problems
spellingShingle MILP-based clustering method for multi-objective optimization: Application to environmental problems
Oliva, Diego Gabriel
Environmental Objectives
Life Cycle Assessment
Multi-Objective Optimization
Pareto Solutions
title_short MILP-based clustering method for multi-objective optimization: Application to environmental problems
title_full MILP-based clustering method for multi-objective optimization: Application to environmental problems
title_fullStr MILP-based clustering method for multi-objective optimization: Application to environmental problems
title_full_unstemmed MILP-based clustering method for multi-objective optimization: Application to environmental problems
title_sort MILP-based clustering method for multi-objective optimization: Application to environmental problems
dc.creator.none.fl_str_mv Oliva, Diego Gabriel
Guillén Gosálbez, G.
Mateo Sanz, J.m.
Jiménez Esteller, L.
author Oliva, Diego Gabriel
author_facet Oliva, Diego Gabriel
Guillén Gosálbez, G.
Mateo Sanz, J.m.
Jiménez Esteller, L.
author_role author
author2 Guillén Gosálbez, G.
Mateo Sanz, J.m.
Jiménez Esteller, L.
author2_role author
author
author
dc.subject.none.fl_str_mv Environmental Objectives
Life Cycle Assessment
Multi-Objective Optimization
Pareto Solutions
topic Environmental Objectives
Life Cycle Assessment
Multi-Objective Optimization
Pareto Solutions
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Multi-objective optimization (MOO) has recently emerged as a useful technique in environmental engineering. One major limitation of this approach is that its computational burden grows rapidly with the number of environmental objectives, which causes difficulties regarding the computation and visualization of the Pareto solutions. In this work we present several theoretical and algorithmic developments for grouping environmental objectives into clusters on the basis of which the multi-objective optimization can be performed, thereby facilitating the computation and analysis of the Pareto solutions. Our method is based on a novel mixed-integer linear program (MILP) that identifies in a systematic manner groups of objectives that behave similarly. We test the capabilities of our approach using several examples, in which we compare it against other well-known clustering methods.
Fil: Oliva, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina. Universitat Rovira I Virgili; España
Fil: Guillén Gosálbez, G.. Universitat Rovira I Virgili; España
Fil: Mateo Sanz, J.m.. Universitat Rovira I Virgili; España
Fil: Jiménez Esteller, L.. Universitat Rovira I Virgili; España
description Multi-objective optimization (MOO) has recently emerged as a useful technique in environmental engineering. One major limitation of this approach is that its computational burden grows rapidly with the number of environmental objectives, which causes difficulties regarding the computation and visualization of the Pareto solutions. In this work we present several theoretical and algorithmic developments for grouping environmental objectives into clusters on the basis of which the multi-objective optimization can be performed, thereby facilitating the computation and analysis of the Pareto solutions. Our method is based on a novel mixed-integer linear program (MILP) that identifies in a systematic manner groups of objectives that behave similarly. We test the capabilities of our approach using several examples, in which we compare it against other well-known clustering methods.
publishDate 2013
dc.date.none.fl_str_mv 2013-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/6919
Oliva, Diego Gabriel; Guillén Gosálbez, G.; Mateo Sanz, J.m.; Jiménez Esteller, L.; MILP-based clustering method for multi-objective optimization: Application to environmental problems; Elsevier; Computers and Chemical Engineering; 56; 9-2013; 202-217
0098-1354
url http://hdl.handle.net/11336/6919
identifier_str_mv Oliva, Diego Gabriel; Guillén Gosálbez, G.; Mateo Sanz, J.m.; Jiménez Esteller, L.; MILP-based clustering method for multi-objective optimization: Application to environmental problems; Elsevier; Computers and Chemical Engineering; 56; 9-2013; 202-217
0098-1354
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2013.05.016
info:eu-repo/semantics/altIdentifier/url/10.1016/j.compchemeng.2013.05.016
info:eu-repo/semantics/altIdentifier/doi/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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