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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23134
Ver los metadatos del registro completo
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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 Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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http://sedici.unlp.edu.ar/handle/10915/23134 |
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http://sedici.unlp.edu.ar/handle/10915/23134 |
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language |
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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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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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