Different evolutionary approaches to solve the flow shop scheduling problem

Autores
Esquivel, Susana Cecilia; Gallard, Raúl Hector; Zuppa, Federico
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Over the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling Problem (FSSP) by means of well-known heuristics which, are successfully used for certain instances of the problem providing a single acceptable solution. Current trends involve distinct evolutionary computation approaches. This work shows [5, 6, 7] implementations of diverse evolutionary approaches on a set of flow shop scheduling instances, including latest approaches using a multirecombination feature, Multiple Crossovers per Couple (MCPC), and partial replacement of the population when possible stagnation is detected. A discussion on implementation details, analysis and a comparison of evolutionary and conventional approaches to the problem are shown.
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Different evolutionary approaches
ARTIFICIAL INTELLIGENCE
Scheduling
flow shop scheduling problem
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/21654
Acceder
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network_name_str SEDICI (UNLP)
spelling Different evolutionary approaches to solve the flow shop scheduling problemEsquivel, Susana CeciliaGallard, Raúl HectorZuppa, FedericoCiencias InformáticasDifferent evolutionary approachesARTIFICIAL INTELLIGENCESchedulingflow shop scheduling problemOver the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling Problem (FSSP) by means of well-known heuristics which, are successfully used for certain instances of the problem providing a single acceptable solution. Current trends involve distinct evolutionary computation approaches. This work shows [5, 6, 7] implementations of diverse evolutionary approaches on a set of flow shop scheduling instances, including latest approaches using a multirecombination feature, Multiple Crossovers per Couple (MCPC), and partial replacement of the population when possible stagnation is detected. A discussion on implementation details, analysis and a comparison of evolutionary and conventional approaches to the problem are shown.Eje: Inteligencia Computacional - MetaheurísticasRed de Universidades con Carreras en Informática (RedUNCI)2001-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/21654enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:54:43Zoai:sedici.unlp.edu.ar:10915/21654Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:43.301SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Different evolutionary approaches to solve the flow shop scheduling problem
title Different evolutionary approaches to solve the flow shop scheduling problem
spellingShingle Different evolutionary approaches to solve the flow shop scheduling problem
Esquivel, Susana Cecilia
Ciencias Informáticas
Different evolutionary approaches
ARTIFICIAL INTELLIGENCE
Scheduling
flow shop scheduling problem
title_short Different evolutionary approaches to solve the flow shop scheduling problem
title_full Different evolutionary approaches to solve the flow shop scheduling problem
title_fullStr Different evolutionary approaches to solve the flow shop scheduling problem
title_full_unstemmed Different evolutionary approaches to solve the flow shop scheduling problem
title_sort Different evolutionary approaches to solve the flow shop scheduling problem
dc.creator.none.fl_str_mv Esquivel, Susana Cecilia
Gallard, Raúl Hector
Zuppa, Federico
author Esquivel, Susana Cecilia
author_facet Esquivel, Susana Cecilia
Gallard, Raúl Hector
Zuppa, Federico
author_role author
author2 Gallard, Raúl Hector
Zuppa, Federico
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Different evolutionary approaches
ARTIFICIAL INTELLIGENCE
Scheduling
flow shop scheduling problem
topic Ciencias Informáticas
Different evolutionary approaches
ARTIFICIAL INTELLIGENCE
Scheduling
flow shop scheduling problem
dc.description.none.fl_txt_mv Over the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling Problem (FSSP) by means of well-known heuristics which, are successfully used for certain instances of the problem providing a single acceptable solution. Current trends involve distinct evolutionary computation approaches. This work shows [5, 6, 7] implementations of diverse evolutionary approaches on a set of flow shop scheduling instances, including latest approaches using a multirecombination feature, Multiple Crossovers per Couple (MCPC), and partial replacement of the population when possible stagnation is detected. A discussion on implementation details, analysis and a comparison of evolutionary and conventional approaches to the problem are shown.
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
description Over the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling Problem (FSSP) by means of well-known heuristics which, are successfully used for certain instances of the problem providing a single acceptable solution. Current trends involve distinct evolutionary computation approaches. This work shows [5, 6, 7] implementations of diverse evolutionary approaches on a set of flow shop scheduling instances, including latest approaches using a multirecombination feature, Multiple Crossovers per Couple (MCPC), and partial replacement of the population when possible stagnation is detected. A discussion on implementation details, analysis and a comparison of evolutionary and conventional approaches to the problem are shown.
publishDate 2001
dc.date.none.fl_str_mv 2001-05
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/21654
url http://sedici.unlp.edu.ar/handle/10915/21654
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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