A genetic algorithm for project scheduling with multi-modes and renewable resources

Detalhes bibliográficos
Autor(a) principal: Magalhães-Mendes, J.
Data de Publicação: 2010
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/4226
Resumo: This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.
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spelling A genetic algorithm for project scheduling with multi-modes and renewable resourcesThis paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.ACMRepositório Científico do Instituto Politécnico do PortoMagalhães-Mendes, J.2014-03-17T16:26:36Z20102010-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/4226eng978-960-474-252-3metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:44:14Zoai:recipp.ipp.pt:10400.22/4226Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:25:06.927370Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A genetic algorithm for project scheduling with multi-modes and renewable resources
title A genetic algorithm for project scheduling with multi-modes and renewable resources
spellingShingle A genetic algorithm for project scheduling with multi-modes and renewable resources
Magalhães-Mendes, J.
title_short A genetic algorithm for project scheduling with multi-modes and renewable resources
title_full A genetic algorithm for project scheduling with multi-modes and renewable resources
title_fullStr A genetic algorithm for project scheduling with multi-modes and renewable resources
title_full_unstemmed A genetic algorithm for project scheduling with multi-modes and renewable resources
title_sort A genetic algorithm for project scheduling with multi-modes and renewable resources
author Magalhães-Mendes, J.
author_facet Magalhães-Mendes, J.
author_role author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Magalhães-Mendes, J.
description This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.
publishDate 2010
dc.date.none.fl_str_mv 2010
2010-01-01T00:00:00Z
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dc.publisher.none.fl_str_mv ACM
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