Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes

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 present a hybrid evolutionary algorithm with self-adaptive processes to solve a known project scheduling problem. This problem takes into consideration an optimization objective priority for project managers: to maximize the effectiveness of the sets of human resources assigned to the project activities. The hybrid evolutionary algorithm integrates self-adaptive processes with the aim of enhancing the evolutionary search. The behavior of these processes is self-adaptive according to the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on six different instance sets and then is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results show that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.
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:11336/6836
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive ProcessesYannibelli, 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 present a hybrid evolutionary algorithm with self-adaptive processes to solve a known project scheduling problem. This problem takes into consideration an optimization objective priority for project managers: to maximize the effectiveness of the sets of human resources assigned to the project activities. The hybrid evolutionary algorithm integrates self-adaptive processes with the aim of enhancing the evolutionary search. The behavior of these processes is self-adaptive according to the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on six different instance sets and then is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results show that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.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; ArgentinaSpringer2015-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/6836Yannibelli, Virginia Daniela; Amandi, Analia Adriana; Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes; Springer; Lecture Notes In Computer Science; 9413; 11-2015; 401-4120302-9743enginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/chapter/10.1007%2F978-3-319-27060-9_33info:eu-repo/semantics/altIdentifier/url/10.1007/978-3-319-27060-9_33info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-27060-9_33info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:28:30Zoai:ri.conicet.gov.ar:11336/6836instacron: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 10:28:31.182CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes
title Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes
spellingShingle Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes
Yannibelli, Virginia Daniela
Project Scheduling
Human Resource Assignment
Multi-Skilled Resources
Hybrid Evolutionary Algorithms
title_short Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes
title_full Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes
title_fullStr Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes
title_full_unstemmed Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes
title_sort Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes
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 present a hybrid evolutionary algorithm with self-adaptive processes to solve a known project scheduling problem. This problem takes into consideration an optimization objective priority for project managers: to maximize the effectiveness of the sets of human resources assigned to the project activities. The hybrid evolutionary algorithm integrates self-adaptive processes with the aim of enhancing the evolutionary search. The behavior of these processes is self-adaptive according to the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on six different instance sets and then is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results show that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.
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 present a hybrid evolutionary algorithm with self-adaptive processes to solve a known project scheduling problem. This problem takes into consideration an optimization objective priority for project managers: to maximize the effectiveness of the sets of human resources assigned to the project activities. The hybrid evolutionary algorithm integrates self-adaptive processes with the aim of enhancing the evolutionary search. The behavior of these processes is self-adaptive according to the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on six different instance sets and then is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results show that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.
publishDate 2015
dc.date.none.fl_str_mv 2015-11
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/11336/6836
Yannibelli, Virginia Daniela; Amandi, Analia Adriana; Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes; Springer; Lecture Notes In Computer Science; 9413; 11-2015; 401-412
0302-9743
url http://hdl.handle.net/11336/6836
identifier_str_mv Yannibelli, Virginia Daniela; Amandi, Analia Adriana; Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes; Springer; Lecture Notes In Computer Science; 9413; 11-2015; 401-412
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%2F978-3-319-27060-9_33
info:eu-repo/semantics/altIdentifier/url/10.1007/978-3-319-27060-9_33
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-27060-9_33
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
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score 13.069144

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