A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.19/7412 |
Resumo: | Telecommunication Company’s (TELCO) are continuously delivering their efforts on the effectiveness of their daily work. Planning the activities for their workers is a crucial sensitive, and time-consuming task usually taken by experts. This plan aims to find an optimized solution maximizing the number of activities assigned to workers and minimizing the inherent costs (e.g., labor from workers, fuel, and other transportation costs). This paper proposes a model that allows computing a maximized plan for the activities assigned to their workers, allowing to alleviate the burden of the existing experts, even if supported by software implementing rule-based heuristic models. The proposed model is inspired by nature and relies on two stages supported by Genetic and Ant Colony evolutionary algorithms. At the first stage, a Genetic Algorithms (GA) identifies the optimal set of activities to be assigned to workers as the way to maximize the revenues. At a second step, an Ant Colony algorithm searches for an efficient path among the activities to minimize the costs. The conducted experimental work validates the effectiveness of the proposed model in the optimization of the planning TELCO work-field activities in comparison to a rule-based heuristic model. |
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A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony AlgorithmsAnt ColonyGenetic AlgorithmsRoute OptimizationTELCOTelecommunication Company’s (TELCO) are continuously delivering their efforts on the effectiveness of their daily work. Planning the activities for their workers is a crucial sensitive, and time-consuming task usually taken by experts. This plan aims to find an optimized solution maximizing the number of activities assigned to workers and minimizing the inherent costs (e.g., labor from workers, fuel, and other transportation costs). This paper proposes a model that allows computing a maximized plan for the activities assigned to their workers, allowing to alleviate the burden of the existing experts, even if supported by software implementing rule-based heuristic models. The proposed model is inspired by nature and relies on two stages supported by Genetic and Ant Colony evolutionary algorithms. At the first stage, a Genetic Algorithms (GA) identifies the optimal set of activities to be assigned to workers as the way to maximize the revenues. At a second step, an Ant Colony algorithm searches for an efficient path among the activities to minimize the costs. The conducted experimental work validates the effectiveness of the proposed model in the optimization of the planning TELCO work-field activities in comparison to a rule-based heuristic model.Repositório Científico do Instituto Politécnico de ViseuHenriques, J.Caldeira, Filipe2022-11-18T11:54:54Z20222022-11-15T18:40:52Z2022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.19/7412engHenriques, J., & Caldeira, F. (2022). A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms. International Journal of Interactive Multimedia and Artificial Intelligence, 7(Special Issue on New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence). https://www.ijimai.org/journal/bibcite/reference/316319891660cv-prod-307902410.9781/ijimai.2022.08.0112-s2.0-85138694146info: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:RCAAP2023-01-16T15:29:33Zoai:repositorio.ipv.pt:10400.19/7412Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:45:08.298859Repositório 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 Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms |
title |
A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms |
spellingShingle |
A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms Henriques, J. Ant Colony Genetic Algorithms Route Optimization TELCO |
title_short |
A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms |
title_full |
A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms |
title_fullStr |
A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms |
title_full_unstemmed |
A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms |
title_sort |
A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms |
author |
Henriques, J. |
author_facet |
Henriques, J. Caldeira, Filipe |
author_role |
author |
author2 |
Caldeira, Filipe |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico de Viseu |
dc.contributor.author.fl_str_mv |
Henriques, J. Caldeira, Filipe |
dc.subject.por.fl_str_mv |
Ant Colony Genetic Algorithms Route Optimization TELCO |
topic |
Ant Colony Genetic Algorithms Route Optimization TELCO |
description |
Telecommunication Company’s (TELCO) are continuously delivering their efforts on the effectiveness of their daily work. Planning the activities for their workers is a crucial sensitive, and time-consuming task usually taken by experts. This plan aims to find an optimized solution maximizing the number of activities assigned to workers and minimizing the inherent costs (e.g., labor from workers, fuel, and other transportation costs). This paper proposes a model that allows computing a maximized plan for the activities assigned to their workers, allowing to alleviate the burden of the existing experts, even if supported by software implementing rule-based heuristic models. The proposed model is inspired by nature and relies on two stages supported by Genetic and Ant Colony evolutionary algorithms. At the first stage, a Genetic Algorithms (GA) identifies the optimal set of activities to be assigned to workers as the way to maximize the revenues. At a second step, an Ant Colony algorithm searches for an efficient path among the activities to minimize the costs. The conducted experimental work validates the effectiveness of the proposed model in the optimization of the planning TELCO work-field activities in comparison to a rule-based heuristic model. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-11-18T11:54:54Z 2022 2022-11-15T18:40:52Z 2022-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.19/7412 |
url |
http://hdl.handle.net/10400.19/7412 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Henriques, J., & Caldeira, F. (2022). A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms. International Journal of Interactive Multimedia and Artificial Intelligence, 7(Special Issue on New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence). https://www.ijimai.org/journal/bibcite/reference/3163 19891660 cv-prod-3079024 10.9781/ijimai.2022.08.011 2-s2.0-85138694146 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799130922768924672 |