Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling

Autores
Yannibelli, Virginia Daniela; Amandi, Analia Adriana
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, we address a project scheduling problem that considers a priority optimization objective for project managers. This objective involves assigning the most effective set of human resources to each project activity. To solve the problem, we propose a hybrid evolutionary algorithm. This algorithm uses adaptive crossover, mutation and simulated annealing processes in order to improve the performance of the evolutionary search. These processes adapt their behavior based on the diversity of the evolutionary algorithm population. We compare the performance of the hybrid evolutionary algorithm with those of the algorithms previously proposed in the literature for solving the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm significantly outperforms the previous algorithms.
Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Materia
Project Scheduling
Human Resource Assignment
Multi-Skilled Resources
Hybrid Evolutionary Algorithms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/6838
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/6838
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project SchedulingYannibelli, Virginia DanielaAmandi, Analia AdrianaProject SchedulingHuman Resource AssignmentMulti-Skilled ResourcesHybrid Evolutionary Algorithmshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this paper, we address a project scheduling problem that considers a priority optimization objective for project managers. This objective involves assigning the most effective set of human resources to each project activity. To solve the problem, we propose a hybrid evolutionary algorithm. This algorithm uses adaptive crossover, mutation and simulated annealing processes in order to improve the performance of the evolutionary search. These processes adapt their behavior based on the diversity of the evolutionary algorithm population. We compare the performance of the hybrid evolutionary algorithm with those of the algorithms previously proposed in the literature for solving the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm significantly outperforms the previous algorithms.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaSpringer2015-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/6838Yannibelli, Virginia Daniela; Amandi, Analia Adriana; Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling; Springer; Lecture Notes In Computer Science; 9375; 9-2015; 340-3510302-9743enginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/chapter/10.1007%2F978-3-319-24834-9_40info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-24834-9_40info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:05:15Zoai:ri.conicet.gov.ar:11336/6838instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:05:15.471CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling
title Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling
spellingShingle Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling
Yannibelli, Virginia Daniela
Project Scheduling
Human Resource Assignment
Multi-Skilled Resources
Hybrid Evolutionary Algorithms
title_short Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling
title_full Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling
title_fullStr Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling
title_full_unstemmed Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling
title_sort Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling
dc.creator.none.fl_str_mv Yannibelli, Virginia Daniela
Amandi, Analia Adriana
author Yannibelli, Virginia Daniela
author_facet Yannibelli, Virginia Daniela
Amandi, Analia Adriana
author_role author
author2 Amandi, Analia Adriana
author2_role author
dc.subject.none.fl_str_mv Project Scheduling
Human Resource Assignment
Multi-Skilled Resources
Hybrid Evolutionary Algorithms
topic Project Scheduling
Human Resource Assignment
Multi-Skilled Resources
Hybrid Evolutionary Algorithms
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper, we address a project scheduling problem that considers a priority optimization objective for project managers. This objective involves assigning the most effective set of human resources to each project activity. To solve the problem, we propose a hybrid evolutionary algorithm. This algorithm uses adaptive crossover, mutation and simulated annealing processes in order to improve the performance of the evolutionary search. These processes adapt their behavior based on the diversity of the evolutionary algorithm population. We compare the performance of the hybrid evolutionary algorithm with those of the algorithms previously proposed in the literature for solving the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm significantly outperforms the previous algorithms.
Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
description In this paper, we address a project scheduling problem that considers a priority optimization objective for project managers. This objective involves assigning the most effective set of human resources to each project activity. To solve the problem, we propose a hybrid evolutionary algorithm. This algorithm uses adaptive crossover, mutation and simulated annealing processes in order to improve the performance of the evolutionary search. These processes adapt their behavior based on the diversity of the evolutionary algorithm population. We compare the performance of the hybrid evolutionary algorithm with those of the algorithms previously proposed in the literature for solving the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm significantly outperforms the previous algorithms.
publishDate 2015
dc.date.none.fl_str_mv 2015-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/6838
Yannibelli, Virginia Daniela; Amandi, Analia Adriana; Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling; Springer; Lecture Notes In Computer Science; 9375; 9-2015; 340-351
0302-9743
url http://hdl.handle.net/11336/6838
identifier_str_mv Yannibelli, Virginia Daniela; Amandi, Analia Adriana; Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling; Springer; Lecture Notes In Computer Science; 9375; 9-2015; 340-351
0302-9743
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/chapter/10.1007%2F978-3-319-24834-9_40
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-24834-9_40
info:eu-repo/semantics/altIdentifier/doi/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613886358061056
score 13.069144

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