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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/21657
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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 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/10915/21657 |
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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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