Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/3618
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dc.contributor.authorLuís Moreira Matiasen
dc.contributor.authorNunes,Ren
dc.contributor.authorMichel Ferreiraen
dc.contributor.authorJoão Mendes Moreiraen
dc.contributor.authorJoão Gamaen
dc.date.accessioned2017-11-20T10:48:14Z-
dc.date.available2017-11-20T10:48:14Z-
dc.date.issued2014en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/3618-
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-08326-1_59en
dc.description.abstractAgent 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. © 2014 Springer International Publishing.en
dc.languageengen
dc.relation6535en
dc.relation5120en
dc.relation5320en
dc.relation5450en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleOn predicting a call center's workload: A discretization-based approachen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:CRACS - Articles in International Conferences
LIAAD - Articles in International Conferences

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