Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems

Autores
Pandolfi, Daniel; San Pedro, María Eugenia de; Villagra, Andrea; Vilanova, Gabriela; Gallard, Raúl Hector
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Jobs to be delivered in a production system are usually weighted according to clients requirements and relevance. Attempting to achieve higher customer satisfaction trends in manufacturing are focussed today on production policies, which emphasizes minimum weighted tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced multiple-crossovers-on-multiple-parents (MCMP) a multirecombinative approach allowing multiple crossovers on the selected pool of (more than two) parents. MCMP-SRI is a novel MCMP variant, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents. Members of this mating pool subsequently undergo multiple crossover operations. This paper briefly describes the weighted tardiness problem in a single machine environment, and summarizes implementation details and MCMP-SRI performance for a set of problem instances extracted from the OR-Library.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Scheduling
Algorithms
ARTIFICIAL INTELLIGENCE
multirecombined evolutionary algorithm
scheduling problems
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:10915/23417
Acceder
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network_name_str SEDICI (UNLP)
spelling Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problemsPandolfi, DanielSan Pedro, María Eugenia deVillagra, AndreaVilanova, GabrielaGallard, Raúl HectorCiencias InformáticasSchedulingAlgorithmsARTIFICIAL INTELLIGENCEmultirecombined evolutionary algorithmscheduling problemsJobs to be delivered in a production system are usually weighted according to clients requirements and relevance. Attempting to achieve higher customer satisfaction trends in manufacturing are focussed today on production policies, which emphasizes minimum weighted tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced multiple-crossovers-on-multiple-parents (MCMP) a multirecombinative approach allowing multiple crossovers on the selected pool of (more than two) parents. MCMP-SRI is a novel MCMP variant, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents. Members of this mating pool subsequently undergo multiple crossover operations. This paper briefly describes the weighted tardiness problem in a single machine environment, and summarizes implementation details and MCMP-SRI performance for a set of problem instances extracted from the OR-Library.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2001-10info: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/10915/23417enginfo: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:UNLP2025-10-15T10:48:01Zoai:sedici.unlp.edu.ar:10915/23417Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:48:01.995SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems
title Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems
spellingShingle Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems
Pandolfi, Daniel
Ciencias Informáticas
Scheduling
Algorithms
ARTIFICIAL INTELLIGENCE
multirecombined evolutionary algorithm
scheduling problems
title_short Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems
title_full Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems
title_fullStr Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems
title_full_unstemmed Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems
title_sort Studs and immigrants in multirecombined evolutionary algorithm to face weighted tardiness scheduling problems
dc.creator.none.fl_str_mv Pandolfi, Daniel
San Pedro, María Eugenia de
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author Pandolfi, Daniel
author_facet Pandolfi, Daniel
San Pedro, María Eugenia de
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author_role author
author2 San Pedro, María Eugenia de
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Scheduling
Algorithms
ARTIFICIAL INTELLIGENCE
multirecombined evolutionary algorithm
scheduling problems
topic Ciencias Informáticas
Scheduling
Algorithms
ARTIFICIAL INTELLIGENCE
multirecombined evolutionary algorithm
scheduling problems
dc.description.none.fl_txt_mv Jobs to be delivered in a production system are usually weighted according to clients requirements and relevance. Attempting to achieve higher customer satisfaction trends in manufacturing are focussed today on production policies, which emphasizes minimum weighted tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced multiple-crossovers-on-multiple-parents (MCMP) a multirecombinative approach allowing multiple crossovers on the selected pool of (more than two) parents. MCMP-SRI is a novel MCMP variant, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents. Members of this mating pool subsequently undergo multiple crossover operations. This paper briefly describes the weighted tardiness problem in a single machine environment, and summarizes implementation details and MCMP-SRI performance for a set of problem instances extracted from the OR-Library.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description Jobs to be delivered in a production system are usually weighted according to clients requirements and relevance. Attempting to achieve higher customer satisfaction trends in manufacturing are focussed today on production policies, which emphasizes minimum weighted tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced multiple-crossovers-on-multiple-parents (MCMP) a multirecombinative approach allowing multiple crossovers on the selected pool of (more than two) parents. MCMP-SRI is a novel MCMP variant, which considers the inclusion of a stud-breeding individual in a pool of random immigrant parents. Members of this mating pool subsequently undergo multiple crossover operations. This paper briefly describes the weighted tardiness problem in a single machine environment, and summarizes implementation details and MCMP-SRI performance for a set of problem instances extracted from the OR-Library.
publishDate 2001
dc.date.none.fl_str_mv 2001-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
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info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23417
url http://sedici.unlp.edu.ar/handle/10915/23417
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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