A Model for Planning TELCO Work-Field Activities Enabled by Genetic and Ant Colony Algorithms

Detalhes bibliográficos
Autor(a) principal: Henriques, J.
Data de Publicação: 2022
Outros Autores: Caldeira, Filipe
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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spelling 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
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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
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