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

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

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

Conference proceedings International

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

Pages: 548-553

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

Roskilde, 25 June 2014 through 27 June 2014

Indexing

Scopus - 1 Citation

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6