A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0

Autores
Rossit, Diego Gabriel; Nesmachnow, Sergio; Rossit, Daniel Alejandro
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Under the novel paradigm of Industry 4.0, missing operations have arisen as a result of the increasingly customization of the industrial products in which customers have an extended control over the characteristics of the final products. As a result, this has completely modified the scheduling and planning management of jobs in modern factories. As a contribution in this area, this article presents a multiobjective evolutionary approach based on decomposition for efficiently addressing the multiobjective flow shop problem with missing operations, a relevant problem in modern industry. Tests performed over a representative set of instances show the competitiveness of the proposed approach when compared with other baseline metaheuristics.
Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Fil: Nesmachnow, Sergio. Universidad de la República; Uruguay
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Materia
INDUSTRY 4.0
FLOW SHOP
MISSING OPERATION
TOTAL TARDINESS
MAKESPAN
EVOLUTIONARY ALGORITHMS
MULTIOBJECTIVE OPTIMIZATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/196113
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0Rossit, Diego GabrielNesmachnow, SergioRossit, Daniel AlejandroINDUSTRY 4.0FLOW SHOPMISSING OPERATIONTOTAL TARDINESSMAKESPANEVOLUTIONARY ALGORITHMSMULTIOBJECTIVE OPTIMIZATIONhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Under the novel paradigm of Industry 4.0, missing operations have arisen as a result of the increasingly customization of the industrial products in which customers have an extended control over the characteristics of the final products. As a result, this has completely modified the scheduling and planning management of jobs in modern factories. As a contribution in this area, this article presents a multiobjective evolutionary approach based on decomposition for efficiently addressing the multiobjective flow shop problem with missing operations, a relevant problem in modern industry. Tests performed over a representative set of instances show the competitiveness of the proposed approach when compared with other baseline metaheuristics.Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Nesmachnow, Sergio. Universidad de la República; UruguayFil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaRam Arti Publishers2022-06-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/196113Rossit, Diego Gabriel; Nesmachnow, Sergio; Rossit, Daniel Alejandro; A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0; Ram Arti Publishers; International Journal of Mathematical, Engineering and Management Sciences; 7; 4; 16-6-2022; 433-4542455-7749CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.ijmems.in/article_detail.php?vid=7&issue_id=33&article_id=439info:eu-repo/semantics/altIdentifier/url/https://doi.org/10.33889/IJMEMS.2022.7.4.029info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:02:19Zoai:ri.conicet.gov.ar:11336/196113instacron: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 10:02:19.958CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0
title A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0
spellingShingle A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0
Rossit, Diego Gabriel
INDUSTRY 4.0
FLOW SHOP
MISSING OPERATION
TOTAL TARDINESS
MAKESPAN
EVOLUTIONARY ALGORITHMS
MULTIOBJECTIVE OPTIMIZATION
title_short A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0
title_full A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0
title_fullStr A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0
title_full_unstemmed A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0
title_sort A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0
dc.creator.none.fl_str_mv Rossit, Diego Gabriel
Nesmachnow, Sergio
Rossit, Daniel Alejandro
author Rossit, Diego Gabriel
author_facet Rossit, Diego Gabriel
Nesmachnow, Sergio
Rossit, Daniel Alejandro
author_role author
author2 Nesmachnow, Sergio
Rossit, Daniel Alejandro
author2_role author
author
dc.subject.none.fl_str_mv INDUSTRY 4.0
FLOW SHOP
MISSING OPERATION
TOTAL TARDINESS
MAKESPAN
EVOLUTIONARY ALGORITHMS
MULTIOBJECTIVE OPTIMIZATION
topic INDUSTRY 4.0
FLOW SHOP
MISSING OPERATION
TOTAL TARDINESS
MAKESPAN
EVOLUTIONARY ALGORITHMS
MULTIOBJECTIVE OPTIMIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Under the novel paradigm of Industry 4.0, missing operations have arisen as a result of the increasingly customization of the industrial products in which customers have an extended control over the characteristics of the final products. As a result, this has completely modified the scheduling and planning management of jobs in modern factories. As a contribution in this area, this article presents a multiobjective evolutionary approach based on decomposition for efficiently addressing the multiobjective flow shop problem with missing operations, a relevant problem in modern industry. Tests performed over a representative set of instances show the competitiveness of the proposed approach when compared with other baseline metaheuristics.
Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Fil: Nesmachnow, Sergio. Universidad de la República; Uruguay
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
description Under the novel paradigm of Industry 4.0, missing operations have arisen as a result of the increasingly customization of the industrial products in which customers have an extended control over the characteristics of the final products. As a result, this has completely modified the scheduling and planning management of jobs in modern factories. As a contribution in this area, this article presents a multiobjective evolutionary approach based on decomposition for efficiently addressing the multiobjective flow shop problem with missing operations, a relevant problem in modern industry. Tests performed over a representative set of instances show the competitiveness of the proposed approach when compared with other baseline metaheuristics.
publishDate 2022
dc.date.none.fl_str_mv 2022-06-16
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/11336/196113
Rossit, Diego Gabriel; Nesmachnow, Sergio; Rossit, Daniel Alejandro; A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0; Ram Arti Publishers; International Journal of Mathematical, Engineering and Management Sciences; 7; 4; 16-6-2022; 433-454
2455-7749
CONICET Digital
CONICET
url http://hdl.handle.net/11336/196113
identifier_str_mv Rossit, Diego Gabriel; Nesmachnow, Sergio; Rossit, Daniel Alejandro; A Multiobjective Evolutionary Algorithm based on Decomposition for a flow shop scheduling problem in the context of Industry 4.0; Ram Arti Publishers; International Journal of Mathematical, Engineering and Management Sciences; 7; 4; 16-6-2022; 433-454
2455-7749
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.ijmems.in/article_detail.php?vid=7&issue_id=33&article_id=439
info:eu-repo/semantics/altIdentifier/url/https://doi.org/10.33889/IJMEMS.2022.7.4.029
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Ram Arti Publishers
publisher.none.fl_str_mv Ram Arti Publishers
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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