Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem

Autores
Stark, Natalia; Salto, Carolina; Alfonso, Hugo; Gallard, Raúl Hector
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Many researchers have shown interest to solve the job shop scheduling problem (JSSP) applying evolutionary algorithms (EAs). In a previous work we reported an enhanced evolutionary algorithm, which uses a multiplicity feature to solve JSSP. The evolutionary approach was enhanced by means of multiple crossovers on multiple parents (MCMP) and the selection of a stud among the intervening parent. Partially mapped crossover (PMX) was used on each multiple crossover operation and job based representation (permutation of jobs) was adopted as a coding technique. The traditional MCMP approach is based on scanning crossover. But the application of this operator to permutations will yield illegal offspring in the sense that some jobs may be missed while some other jobs may be duplicated in the offspring, so some modifications to their mechanism are necessary to guarantee the offspring legality. This paper contrasts both MCMP approaches, discusses implementation details and shows results for a set of job shop scheduling instances of distinct complexity.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Optimization
Scheduling
ARTIFICIAL INTELLIGENCE
scanning crossover
breeding
Multirecombination
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:10915/23413
Acceder
id SEDICI_e663cc67a2bc62a6db089769f287ee79
oai_identifier_str oai:sedici.unlp.edu.ar:10915/23413
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problemStark, NataliaSalto, CarolinaAlfonso, HugoGallard, Raúl HectorCiencias InformáticasOptimizationSchedulingARTIFICIAL INTELLIGENCEscanning crossoverbreedingMultirecombinationMany researchers have shown interest to solve the job shop scheduling problem (JSSP) applying evolutionary algorithms (EAs). In a previous work we reported an enhanced evolutionary algorithm, which uses a multiplicity feature to solve JSSP. The evolutionary approach was enhanced by means of multiple crossovers on multiple parents (MCMP) and the selection of a stud among the intervening parent. Partially mapped crossover (PMX) was used on each multiple crossover operation and job based representation (permutation of jobs) was adopted as a coding technique. The traditional MCMP approach is based on scanning crossover. But the application of this operator to permutations will yield illegal offspring in the sense that some jobs may be missed while some other jobs may be duplicated in the offspring, so some modifications to their mechanism are necessary to guarantee the offspring legality. This paper contrasts both MCMP approaches, discusses implementation details and shows results for a set of job shop scheduling instances of distinct complexity.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/10915/23413enginfo: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:UNLP2025-09-29T10:55:26Zoai:sedici.unlp.edu.ar:10915/23413Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:26.742SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
spellingShingle Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
Stark, Natalia
Ciencias Informáticas
Optimization
Scheduling
ARTIFICIAL INTELLIGENCE
scanning crossover
breeding
Multirecombination
title_short Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title_full Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title_fullStr Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title_full_unstemmed Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
title_sort Contrasting two MCMP alternatives in evolutionary algorithms to solve the job shop scheduling problem
dc.creator.none.fl_str_mv Stark, Natalia
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author Stark, Natalia
author_facet Stark, Natalia
Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author_role author
author2 Salto, Carolina
Alfonso, Hugo
Gallard, Raúl Hector
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Optimization
Scheduling
ARTIFICIAL INTELLIGENCE
scanning crossover
breeding
Multirecombination
topic Ciencias Informáticas
Optimization
Scheduling
ARTIFICIAL INTELLIGENCE
scanning crossover
breeding
Multirecombination
dc.description.none.fl_txt_mv Many researchers have shown interest to solve the job shop scheduling problem (JSSP) applying evolutionary algorithms (EAs). In a previous work we reported an enhanced evolutionary algorithm, which uses a multiplicity feature to solve JSSP. The evolutionary approach was enhanced by means of multiple crossovers on multiple parents (MCMP) and the selection of a stud among the intervening parent. Partially mapped crossover (PMX) was used on each multiple crossover operation and job based representation (permutation of jobs) was adopted as a coding technique. The traditional MCMP approach is based on scanning crossover. But the application of this operator to permutations will yield illegal offspring in the sense that some jobs may be missed while some other jobs may be duplicated in the offspring, so some modifications to their mechanism are necessary to guarantee the offspring legality. This paper contrasts both MCMP approaches, discusses implementation details and shows results for a set of job shop scheduling instances of distinct complexity.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description Many researchers have shown interest to solve the job shop scheduling problem (JSSP) applying evolutionary algorithms (EAs). In a previous work we reported an enhanced evolutionary algorithm, which uses a multiplicity feature to solve JSSP. The evolutionary approach was enhanced by means of multiple crossovers on multiple parents (MCMP) and the selection of a stud among the intervening parent. Partially mapped crossover (PMX) was used on each multiple crossover operation and job based representation (permutation of jobs) was adopted as a coding technique. The traditional MCMP approach is based on scanning crossover. But the application of this operator to permutations will yield illegal offspring in the sense that some jobs may be missed while some other jobs may be duplicated in the offspring, so some modifications to their mechanism are necessary to guarantee the offspring legality. This paper contrasts both MCMP approaches, discusses implementation details and shows results for a set of job shop scheduling instances of distinct complexity.
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
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/10915/23413
url http://sedici.unlp.edu.ar/handle/10915/23413
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
_version_ 1844615813293670400
score 13.069144

Publicaciones similares