Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems

Autores
San Pedro, María Eugenia de; Pandolfi, Daniel; Villagra, Andrea; Lasso, Marta Graciela; Vilanova, Gabriela; Gallard, Raúl Hector
Año de publicación
2002
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In a production system it is usual to stress minimum tardiness to achieve higher client satisfaction. According to the client relevance, job processing costs and requirements, and various other considerations, a weight is assigned to each job. An important, non-trivial, problem is to minimize weighted tardiness. Evolutionary algorithms (EAs) have been proved as efficient tools to solve scheduling problems. Latest improvements in EAs have been developed by means of multirecombination, a method which allows multiple exchange of genetic material between individuals of the mating pool. As EAs are blind search methods this paper proposes to insert problem-specific-knowledge by recombining potential solutions (individuals of the evolving population) with seeds, which are solutions provided by other heuristics specifically intended to solve the scheduling problem under study. In this work we describe two main approaches where seeds are inserted either in the initial population or as a part of every mating pool during evolution. Both methods were contrasted for a set of problem instances extracted from the OR-Library. An outline of the weighted tardiness problem in a single machine environment, details of implementation and results are discussed.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Evolutionary Algorithms
Algorithms
Solve W-T Scheduling Problems
Scheduling
ARTIFICIAL INTELLIGENCE
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:10915/23134

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spelling Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problemsSan Pedro, María Eugenia dePandolfi, DanielVillagra, AndreaLasso, Marta GracielaVilanova, GabrielaGallard, Raúl HectorCiencias InformáticasEvolutionary AlgorithmsAlgorithmsSolve W-T Scheduling ProblemsSchedulingARTIFICIAL INTELLIGENCEIn a production system it is usual to stress minimum tardiness to achieve higher client satisfaction. According to the client relevance, job processing costs and requirements, and various other considerations, a weight is assigned to each job. An important, non-trivial, problem is to minimize weighted tardiness. Evolutionary algorithms (EAs) have been proved as efficient tools to solve scheduling problems. Latest improvements in EAs have been developed by means of multirecombination, a method which allows multiple exchange of genetic material between individuals of the mating pool. As EAs are blind search methods this paper proposes to insert problem-specific-knowledge by recombining potential solutions (individuals of the evolving population) with seeds, which are solutions provided by other heuristics specifically intended to solve the scheduling problem under study. In this work we describe two main approaches where seeds are inserted either in the initial population or as a part of every mating pool during evolution. Both methods were contrasted for a set of problem instances extracted from the OR-Library. An outline of the weighted tardiness problem in a single machine environment, details of implementation and results are discussed.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2002-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf343-353http://sedici.unlp.edu.ar/handle/10915/23134spainfo: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:UNLP2025-09-29T10:55:20Zoai:sedici.unlp.edu.ar:10915/23134Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:21.078SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
spellingShingle Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
San Pedro, María Eugenia de
Ciencias Informáticas
Evolutionary Algorithms
Algorithms
Solve W-T Scheduling Problems
Scheduling
ARTIFICIAL INTELLIGENCE
title_short Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title_full Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title_fullStr Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title_full_unstemmed Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
title_sort Adding problem-specific knowledge in evolutionary algorithms to solve W-T scheduling problems
dc.creator.none.fl_str_mv San Pedro, María Eugenia de
Pandolfi, Daniel
Villagra, Andrea
Lasso, Marta Graciela
Vilanova, Gabriela
Gallard, Raúl Hector
author San Pedro, María Eugenia de
author_facet San Pedro, María Eugenia de
Pandolfi, Daniel
Villagra, Andrea
Lasso, Marta Graciela
Vilanova, Gabriela
Gallard, Raúl Hector
author_role author
author2 Pandolfi, Daniel
Villagra, Andrea
Lasso, Marta Graciela
Vilanova, Gabriela
Gallard, Raúl Hector
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Evolutionary Algorithms
Algorithms
Solve W-T Scheduling Problems
Scheduling
ARTIFICIAL INTELLIGENCE
topic Ciencias Informáticas
Evolutionary Algorithms
Algorithms
Solve W-T Scheduling Problems
Scheduling
ARTIFICIAL INTELLIGENCE
dc.description.none.fl_txt_mv In a production system it is usual to stress minimum tardiness to achieve higher client satisfaction. According to the client relevance, job processing costs and requirements, and various other considerations, a weight is assigned to each job. An important, non-trivial, problem is to minimize weighted tardiness. Evolutionary algorithms (EAs) have been proved as efficient tools to solve scheduling problems. Latest improvements in EAs have been developed by means of multirecombination, a method which allows multiple exchange of genetic material between individuals of the mating pool. As EAs are blind search methods this paper proposes to insert problem-specific-knowledge by recombining potential solutions (individuals of the evolving population) with seeds, which are solutions provided by other heuristics specifically intended to solve the scheduling problem under study. In this work we describe two main approaches where seeds are inserted either in the initial population or as a part of every mating pool during evolution. Both methods were contrasted for a set of problem instances extracted from the OR-Library. An outline of the weighted tardiness problem in a single machine environment, details of implementation and results are discussed.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description In a production system it is usual to stress minimum tardiness to achieve higher client satisfaction. According to the client relevance, job processing costs and requirements, and various other considerations, a weight is assigned to each job. An important, non-trivial, problem is to minimize weighted tardiness. Evolutionary algorithms (EAs) have been proved as efficient tools to solve scheduling problems. Latest improvements in EAs have been developed by means of multirecombination, a method which allows multiple exchange of genetic material between individuals of the mating pool. As EAs are blind search methods this paper proposes to insert problem-specific-knowledge by recombining potential solutions (individuals of the evolving population) with seeds, which are solutions provided by other heuristics specifically intended to solve the scheduling problem under study. In this work we describe two main approaches where seeds are inserted either in the initial population or as a part of every mating pool during evolution. Both methods were contrasted for a set of problem instances extracted from the OR-Library. An outline of the weighted tardiness problem in a single machine environment, details of implementation and results are discussed.
publishDate 2002
dc.date.none.fl_str_mv 2002-10
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/10915/23134
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language spa
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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343-353
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