Adaptability of multirecombinated evolutionary algorithms to changing common due dates

Autores
Pandolfi, Daniel; Vilanova, Gabriela; San Pedro, María Eugenia de; Villagra, Andrea; Gallard, Raúl Hector
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In the restricted single-machine common due date problem, the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties. This problem, even in its simplest formulation, is an NP-Hard optimization problem. 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-V is a novel MCMP variant, which directly applies multirecombination to the Lee and Kim approach using uniform scanning crossover.
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Evolutionary Algorithms
Single Machine Scheduling
Multirecombination
Common due date
Problem
ARTIFICIAL INTELLIGENCE
Algorithms
Scheduling
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:10915/21650
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/21650
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Adaptability of multirecombinated evolutionary algorithms to changing common due datesPandolfi, DanielVilanova, GabrielaSan Pedro, María Eugenia deVillagra, AndreaGallard, Raúl HectorCiencias InformáticasEvolutionary AlgorithmsSingle Machine SchedulingMultirecombinationCommon due dateProblemARTIFICIAL INTELLIGENCEAlgorithmsSchedulingIn the restricted single-machine common due date problem, the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties. This problem, even in its simplest formulation, is an NP-Hard optimization problem. 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-V is a novel MCMP variant, which directly applies multirecombination to the Lee and Kim approach using uniform scanning crossover.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/10915/21650enginfo: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:10915/21650Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:43.287SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Adaptability of multirecombinated evolutionary algorithms to changing common due dates
title Adaptability of multirecombinated evolutionary algorithms to changing common due dates
spellingShingle Adaptability of multirecombinated evolutionary algorithms to changing common due dates
Pandolfi, Daniel
Ciencias Informáticas
Evolutionary Algorithms
Single Machine Scheduling
Multirecombination
Common due date
Problem
ARTIFICIAL INTELLIGENCE
Algorithms
Scheduling
title_short Adaptability of multirecombinated evolutionary algorithms to changing common due dates
title_full Adaptability of multirecombinated evolutionary algorithms to changing common due dates
title_fullStr Adaptability of multirecombinated evolutionary algorithms to changing common due dates
title_full_unstemmed Adaptability of multirecombinated evolutionary algorithms to changing common due dates
title_sort Adaptability of multirecombinated evolutionary algorithms to changing common due dates
dc.creator.none.fl_str_mv Pandolfi, Daniel
Vilanova, Gabriela
San Pedro, María Eugenia de
Villagra, Andrea
Gallard, Raúl Hector
author Pandolfi, Daniel
author_facet Pandolfi, Daniel
Vilanova, Gabriela
San Pedro, María Eugenia de
Villagra, Andrea
Gallard, Raúl Hector
author_role author
author2 Vilanova, Gabriela
San Pedro, María Eugenia de
Villagra, Andrea
Gallard, Raúl Hector
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Evolutionary Algorithms
Single Machine Scheduling
Multirecombination
Common due date
Problem
ARTIFICIAL INTELLIGENCE
Algorithms
Scheduling
topic Ciencias Informáticas
Evolutionary Algorithms
Single Machine Scheduling
Multirecombination
Common due date
Problem
ARTIFICIAL INTELLIGENCE
Algorithms
Scheduling
dc.description.none.fl_txt_mv In the restricted single-machine common due date problem, the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties. This problem, even in its simplest formulation, is an NP-Hard optimization problem. 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-V is a novel MCMP variant, which directly applies multirecombination to the Lee and Kim approach using uniform scanning crossover.
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
description In the restricted single-machine common due date problem, the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties. This problem, even in its simplest formulation, is an NP-Hard optimization problem. 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-V is a novel MCMP variant, which directly applies multirecombination to the Lee and Kim approach using uniform scanning crossover.
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
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/21650
url http://sedici.unlp.edu.ar/handle/10915/21650
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
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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