Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling Problem

Detalhes bibliográficos
Autor(a) principal: Motta Toledo, Claudio Fabiano
Data de Publicação: 2008
Outros Autores: Franga, Paulo Morelato [UNESP], Rosa, Kalianne Almeida, ACM
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/195936
Resumo: This paper studies the use of different population structures in a Genetic Algorithm (GA) applied to lot sizing and scheduling problems. The population approaches are divided into two types: single-population and multi-population. The first type has a non-structured single population. The multi-population type presents non-structured and structured populations organized in binary and ternary trees. Each population approach is tested on lot sizing and scheduling problems found in soft drink companies. These problems have two interdependent levels with decisions concerning raw material storage and soft drink bottling. The challenge is to simultaneously determine the lot sizing and scheduling of raw materials in tanks and products in lines. Computational results are reported allowing determining the better population structure for the set of problem instances evaluated.
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spelling Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling ProblemGenetic algorithmsMulti-populationLot sizingSchedulingSoft drink companyThis paper studies the use of different population structures in a Genetic Algorithm (GA) applied to lot sizing and scheduling problems. The population approaches are divided into two types: single-population and multi-population. The first type has a non-structured single population. The multi-population type presents non-structured and structured populations organized in binary and ternary trees. Each population approach is tested on lot sizing and scheduling problems found in soft drink companies. These problems have two interdependent levels with decisions concerning raw material storage and soft drink bottling. The challenge is to simultaneously determine the lot sizing and scheduling of raw materials in tanks and products in lines. Computational results are reported allowing determining the better population structure for the set of problem instances evaluated.Univ Fed Lavras, Dept Ciencia Comp, BR-37200000 Lavras, MG, BrazilUniv Estadual Paulista, Dept Mat Estat & Comp, BR-19060900 P Prudente, SP, BrazilUniv Estadual Paulista, Dept Mat Estat & Comp, BR-19060900 P Prudente, SP, BrazilAssoc Computing MachineryUniversidade Federal de Lavras (UFLA)Universidade Estadual Paulista (Unesp)Motta Toledo, Claudio FabianoFranga, Paulo Morelato [UNESP]Rosa, Kalianne AlmeidaACM2020-12-10T18:12:48Z2020-12-10T18:12:48Z2008-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1777-+Applied Computing 2008, Vols 1-3. New York: Assoc Computing Machinery, p. 1777-+, 2008.http://hdl.handle.net/11449/195936WOS:000268392202027Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Computing 2008, Vols 1-3info:eu-repo/semantics/openAccess2024-06-18T18:18:36Zoai:repositorio.unesp.br:11449/195936Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:03:32.716430Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling Problem
title Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling Problem
spellingShingle Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling Problem
Motta Toledo, Claudio Fabiano
Genetic algorithms
Multi-population
Lot sizing
Scheduling
Soft drink company
title_short Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling Problem
title_full Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling Problem
title_fullStr Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling Problem
title_full_unstemmed Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling Problem
title_sort Evaluating Genetic Algorithms with Different Population Structures on a Lot Sizing and Scheduling Problem
author Motta Toledo, Claudio Fabiano
author_facet Motta Toledo, Claudio Fabiano
Franga, Paulo Morelato [UNESP]
Rosa, Kalianne Almeida
ACM
author_role author
author2 Franga, Paulo Morelato [UNESP]
Rosa, Kalianne Almeida
ACM
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de Lavras (UFLA)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Motta Toledo, Claudio Fabiano
Franga, Paulo Morelato [UNESP]
Rosa, Kalianne Almeida
ACM
dc.subject.por.fl_str_mv Genetic algorithms
Multi-population
Lot sizing
Scheduling
Soft drink company
topic Genetic algorithms
Multi-population
Lot sizing
Scheduling
Soft drink company
description This paper studies the use of different population structures in a Genetic Algorithm (GA) applied to lot sizing and scheduling problems. The population approaches are divided into two types: single-population and multi-population. The first type has a non-structured single population. The multi-population type presents non-structured and structured populations organized in binary and ternary trees. Each population approach is tested on lot sizing and scheduling problems found in soft drink companies. These problems have two interdependent levels with decisions concerning raw material storage and soft drink bottling. The challenge is to simultaneously determine the lot sizing and scheduling of raw materials in tanks and products in lines. Computational results are reported allowing determining the better population structure for the set of problem instances evaluated.
publishDate 2008
dc.date.none.fl_str_mv 2008-01-01
2020-12-10T18:12:48Z
2020-12-10T18:12:48Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv Applied Computing 2008, Vols 1-3. New York: Assoc Computing Machinery, p. 1777-+, 2008.
http://hdl.handle.net/11449/195936
WOS:000268392202027
identifier_str_mv Applied Computing 2008, Vols 1-3. New York: Assoc Computing Machinery, p. 1777-+, 2008.
WOS:000268392202027
url http://hdl.handle.net/11449/195936
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Applied Computing 2008, Vols 1-3
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1777-+
dc.publisher.none.fl_str_mv Assoc Computing Machinery
publisher.none.fl_str_mv Assoc Computing Machinery
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808128601684443136

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