Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problems

Autores
San Pedro, María Eugenia de; Pandolfi, Daniel; Villagra, Andrea; Vilanova, Gabriela; Gallard, Raúl Hector
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The study of earliness and tardiness penalties in scheduling is a relatively recent area of research. In the past, traditionally the emphasis was put on regular measures that are nondecreasing in job completion times such as makespan, mean lateness, percentage of tardy jobs or mean tardiness. Current trends in manufacturing is focussed in just-in-time production which emphasize policies discouraging earliness as well as tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced (MCMP) a multirecombinative approach allowing multiple-crossovers-on-multiple-parents (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 describes implementation details and the performance of MCMP-SRI for a set of single machine scheduling instances with a common due date.
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Evolución
Algorithms
Scheduling
evolutionary algorithms
random immigrants
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:10915/21657

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spelling Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problemsSan Pedro, María Eugenia dePandolfi, DanielVillagra, AndreaVilanova, GabrielaGallard, Raúl HectorCiencias InformáticasARTIFICIAL INTELLIGENCEEvoluciónAlgorithmsSchedulingevolutionary algorithmsrandom immigrantsscheduling problemsThe study of earliness and tardiness penalties in scheduling is a relatively recent area of research. In the past, traditionally the emphasis was put on regular measures that are nondecreasing in job completion times such as makespan, mean lateness, percentage of tardy jobs or mean tardiness. Current trends in manufacturing is focussed in just-in-time production which emphasize policies discouraging earliness as well as tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced (MCMP) a multirecombinative approach allowing multiple-crossovers-on-multiple-parents (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 describes implementation details and the performance of MCMP-SRI for a set of single machine scheduling instances with a common due date.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/10915/21657enginfo: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:10915/21657Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:43.31SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problems
title Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problems
spellingShingle Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problems
San Pedro, María Eugenia de
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Evolución
Algorithms
Scheduling
evolutionary algorithms
random immigrants
scheduling problems
title_short Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problems
title_full Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problems
title_fullStr Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problems
title_full_unstemmed Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problems
title_sort Evolutionary algorithms whit studs and random immigrants to solve E/T scheduling problems
dc.creator.none.fl_str_mv San Pedro, María Eugenia de
Pandolfi, Daniel
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author San Pedro, María Eugenia de
author_facet San Pedro, María Eugenia de
Pandolfi, Daniel
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author_role author
author2 Pandolfi, Daniel
Villagra, Andrea
Vilanova, Gabriela
Gallard, Raúl Hector
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Evolución
Algorithms
Scheduling
evolutionary algorithms
random immigrants
scheduling problems
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Evolución
Algorithms
Scheduling
evolutionary algorithms
random immigrants
scheduling problems
dc.description.none.fl_txt_mv The study of earliness and tardiness penalties in scheduling is a relatively recent area of research. In the past, traditionally the emphasis was put on regular measures that are nondecreasing in job completion times such as makespan, mean lateness, percentage of tardy jobs or mean tardiness. Current trends in manufacturing is focussed in just-in-time production which emphasize policies discouraging earliness as well as tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced (MCMP) a multirecombinative approach allowing multiple-crossovers-on-multiple-parents (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 describes implementation details and the performance of MCMP-SRI for a set of single machine scheduling instances with a common due date.
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
description The study of earliness and tardiness penalties in scheduling is a relatively recent area of research. In the past, traditionally the emphasis was put on regular measures that are nondecreasing in job completion times such as makespan, mean lateness, percentage of tardy jobs or mean tardiness. Current trends in manufacturing is focussed in just-in-time production which emphasize policies discouraging earliness as well as tardiness. Evolutionary algorithms have been successfully applied to solve scheduling problems. New trends to enhance evolutionary algorithms introduced (MCMP) a multirecombinative approach allowing multiple-crossovers-on-multiple-parents (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 describes implementation details and the performance of MCMP-SRI for a set of single machine scheduling instances with a common due date.
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/10915/21657
url http://sedici.unlp.edu.ar/handle/10915/21657
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
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