On predicting a call center's workload: A discretization-based approach
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/82912 |
Resumo: | Agent scheduling in call centers is a major management problem as the optimal ratio between service quality and costs is hardly achieved. In the literature, regression and time series analysis methods have been used to address this problem by predicting the future arrival counts. In this paper, we propose to discretize these target variables into finite intervals. By reducing its domain length, the goal is to accurately mine the demand peaks as these are the main cause for abandoned calls. This was done by employing multi-class classification. This approach was tested on a real-world dataset acquired through a taxi dispatching call center. The results demonstrate that this framework can accurately reduce the number of abandoned calls, while maintaining a reasonable staff-based cost. Â(c) 2014 Springer International Publishing. |
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On predicting a call center's workload: A discretization-based approachInteligência artificial, Ciências da computação e da informaçãoArtificial intelligence, Computer and information sciencesAgent scheduling in call centers is a major management problem as the optimal ratio between service quality and costs is hardly achieved. In the literature, regression and time series analysis methods have been used to address this problem by predicting the future arrival counts. In this paper, we propose to discretize these target variables into finite intervals. By reducing its domain length, the goal is to accurately mine the demand peaks as these are the main cause for abandoned calls. This was done by employing multi-class classification. This approach was tested on a real-world dataset acquired through a taxi dispatching call center. The results demonstrate that this framework can accurately reduce the number of abandoned calls, while maintaining a reasonable staff-based cost. Â(c) 2014 Springer International Publishing.20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/82912eng10.1007/978-3-319-08326-1_59Luís Moreira MatiasRafael NunesMichel FerreiraJoão Mendes MoreiraJoão Gamainfo: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:RCAAP2023-11-29T15:03:51Zoai:repositorio-aberto.up.pt:10216/82912Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:14:47.732372Repositório 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 |
On predicting a call center's workload: A discretization-based approach |
title |
On predicting a call center's workload: A discretization-based approach |
spellingShingle |
On predicting a call center's workload: A discretization-based approach Luís Moreira Matias Inteligência artificial, Ciências da computação e da informação Artificial intelligence, Computer and information sciences |
title_short |
On predicting a call center's workload: A discretization-based approach |
title_full |
On predicting a call center's workload: A discretization-based approach |
title_fullStr |
On predicting a call center's workload: A discretization-based approach |
title_full_unstemmed |
On predicting a call center's workload: A discretization-based approach |
title_sort |
On predicting a call center's workload: A discretization-based approach |
author |
Luís Moreira Matias |
author_facet |
Luís Moreira Matias Rafael Nunes Michel Ferreira João Mendes Moreira João Gama |
author_role |
author |
author2 |
Rafael Nunes Michel Ferreira João Mendes Moreira João Gama |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Luís Moreira Matias Rafael Nunes Michel Ferreira João Mendes Moreira João Gama |
dc.subject.por.fl_str_mv |
Inteligência artificial, Ciências da computação e da informação Artificial intelligence, Computer and information sciences |
topic |
Inteligência artificial, Ciências da computação e da informação Artificial intelligence, Computer and information sciences |
description |
Agent scheduling in call centers is a major management problem as the optimal ratio between service quality and costs is hardly achieved. In the literature, regression and time series analysis methods have been used to address this problem by predicting the future arrival counts. In this paper, we propose to discretize these target variables into finite intervals. By reducing its domain length, the goal is to accurately mine the demand peaks as these are the main cause for abandoned calls. This was done by employing multi-class classification. This approach was tested on a real-world dataset acquired through a taxi dispatching call center. The results demonstrate that this framework can accurately reduce the number of abandoned calls, while maintaining a reasonable staff-based cost. Â(c) 2014 Springer International Publishing. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014 2014-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/82912 |
url |
https://hdl.handle.net/10216/82912 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/978-3-319-08326-1_59 |
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 |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
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 |
repository.mail.fl_str_mv |
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1799136068826562560 |