On predicting a call center's workload: A discretization-based approach

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Luís Moreira Matias (Autor) Outra; Rafael Nunes (Autor) FCUP; Michel Ferreira (Autor) FCUP; João Mendes Moreira (Autor) FEUP; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Páginas: 548-553

21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014

Roskilde, 25 June 2014 through 27 June 2014

Indexação

Scopus - 1 Citação

INSPEC

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

Outras Informações

ID Authenticus: P-009-NM6

DOI: 10.1007/978-3-319-08326-1_59

Abstract (EN): 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. © 2014 Springer International Publishing.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6