Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling
- Autores
- Esquivel, Susana Cecilia; Ferrero, Sergio W.; Gallard, Raúl Hector
- Año de publicación
- 2000
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Multiobjective optimization, also known as vector-valued criteria or multicriteria optimization, have long been used in many application areas where a problem involves multiple objectives, often conflicting, to be met or optimized. Scheduling problems is one of such application areas whose importance lays on its economical impact and its complexity. The present paper propases CPS-MCPC, a cooperative population search method with multiple crossovers per couple. The cooperati ve search CPS is implemented with in di viduals of a single population, which are selected for recombination using alternatively each criterion. MCPC a multirecombination approach is used to exploit good features of both selected parents. To test the potentials of the novel method for building the Pareto front regular and non-regular objectives functions were chosen: the makespan and the mean absolute deviation of job completion times from a common due date (an earliness/ tardiness related problem). The set of experiments conducted, used three basic representation schemes and contrasted results of the proposed approach against conventional methods of recombination. Details of implementation and results are discussed.
I Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
evolutionary computation
job shop scheduling
multiobjective optimization
multirecombination - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23418
Ver los metadatos del registro completo
id |
SEDICI_4bb9a375ad17a19e2ab21502ccdee7bf |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/23418 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
spelling |
Multirecombinated evolutionary algorithms to solve multiobjective job shop schedulingEsquivel, Susana CeciliaFerrero, Sergio W.Gallard, Raúl HectorCiencias Informáticasevolutionary computationjob shop schedulingmultiobjective optimizationmultirecombinationMultiobjective optimization, also known as vector-valued criteria or multicriteria optimization, have long been used in many application areas where a problem involves multiple objectives, often conflicting, to be met or optimized. Scheduling problems is one of such application areas whose importance lays on its economical impact and its complexity. The present paper propases CPS-MCPC, a cooperative population search method with multiple crossovers per couple. The cooperati ve search CPS is implemented with in di viduals of a single population, which are selected for recombination using alternatively each criterion. MCPC a multirecombination approach is used to exploit good features of both selected parents. To test the potentials of the novel method for building the Pareto front regular and non-regular objectives functions were chosen: the makespan and the mean absolute deviation of job completion times from a common due date (an earliness/ tardiness related problem). The set of experiments conducted, used three basic representation schemes and contrasted results of the proposed approach against conventional methods of recombination. Details of implementation and results are discussed.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2000-10info: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/10915/23418enginfo: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:UNLP2025-09-03T10:28:16Zoai:sedici.unlp.edu.ar:10915/23418Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:17.164SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling |
title |
Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling |
spellingShingle |
Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling Esquivel, Susana Cecilia Ciencias Informáticas evolutionary computation job shop scheduling multiobjective optimization multirecombination |
title_short |
Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling |
title_full |
Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling |
title_fullStr |
Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling |
title_full_unstemmed |
Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling |
title_sort |
Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling |
dc.creator.none.fl_str_mv |
Esquivel, Susana Cecilia Ferrero, Sergio W. Gallard, Raúl Hector |
author |
Esquivel, Susana Cecilia |
author_facet |
Esquivel, Susana Cecilia Ferrero, Sergio W. Gallard, Raúl Hector |
author_role |
author |
author2 |
Ferrero, Sergio W. Gallard, Raúl Hector |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas evolutionary computation job shop scheduling multiobjective optimization multirecombination |
topic |
Ciencias Informáticas evolutionary computation job shop scheduling multiobjective optimization multirecombination |
dc.description.none.fl_txt_mv |
Multiobjective optimization, also known as vector-valued criteria or multicriteria optimization, have long been used in many application areas where a problem involves multiple objectives, often conflicting, to be met or optimized. Scheduling problems is one of such application areas whose importance lays on its economical impact and its complexity. The present paper propases CPS-MCPC, a cooperative population search method with multiple crossovers per couple. The cooperati ve search CPS is implemented with in di viduals of a single population, which are selected for recombination using alternatively each criterion. MCPC a multirecombination approach is used to exploit good features of both selected parents. To test the potentials of the novel method for building the Pareto front regular and non-regular objectives functions were chosen: the makespan and the mean absolute deviation of job completion times from a common due date (an earliness/ tardiness related problem). The set of experiments conducted, used three basic representation schemes and contrasted results of the proposed approach against conventional methods of recombination. Details of implementation and results are discussed. I Workshop de Agentes y Sistemas Inteligentes (WASI) Red de Universidades con Carreras en Informática (RedUNCI) |
description |
Multiobjective optimization, also known as vector-valued criteria or multicriteria optimization, have long been used in many application areas where a problem involves multiple objectives, often conflicting, to be met or optimized. Scheduling problems is one of such application areas whose importance lays on its economical impact and its complexity. The present paper propases CPS-MCPC, a cooperative population search method with multiple crossovers per couple. The cooperati ve search CPS is implemented with in di viduals of a single population, which are selected for recombination using alternatively each criterion. MCPC a multirecombination approach is used to exploit good features of both selected parents. To test the potentials of the novel method for building the Pareto front regular and non-regular objectives functions were chosen: the makespan and the mean absolute deviation of job completion times from a common due date (an earliness/ tardiness related problem). The set of experiments conducted, used three basic representation schemes and contrasted results of the proposed approach against conventional methods of recombination. Details of implementation and results are discussed. |
publishDate |
2000 |
dc.date.none.fl_str_mv |
2000-10 |
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/10915/23418 |
url |
http://sedici.unlp.edu.ar/handle/10915/23418 |
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_ |
1842260121135087616 |
score |
13.13397 |