Delay Predictors in Multi-skill Call Centers: An Empirical Comparison with Real Data

Mamadou Thiongane, Wyean Chan, Pierre L’Ecuyer

Abstract

We examine and compare different delay predictors for multi-skill call centers. Each time a new call (customer) arrives, a predictor takes as input some observable information from the current state of the system, and returns as output a forecast of the waiting time for this call, which is an estimate of the expected waiting time conditional on the current state. Any relevant observable information can be included, e.g., the time of the day, the set of agents at work, the queue size for each call type, the waiting times of the most recent calls who started their service, etc. We consider predictors based on delay history, regularized regression, cubic spline regression, and deep feedforward artificial neural networks. We compare them using real data obtained from a call center. We also examine the issue of how to select the input variables for the predictors.

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Paper Citation


in Harvard Style

Thiongane M., Chan W. and L’Ecuyer P. (2020). Delay Predictors in Multi-skill Call Centers: An Empirical Comparison with Real Data.In Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-396-4, pages 100-108. DOI: 10.5220/0009181401000108


in Bibtex Style

@conference{icores20,
author={Mamadou Thiongane and Wyean Chan and Pierre L’Ecuyer},
title={Delay Predictors in Multi-skill Call Centers: An Empirical Comparison with Real Data},
booktitle={Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2020},
pages={100-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009181401000108},
isbn={978-989-758-396-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Delay Predictors in Multi-skill Call Centers: An Empirical Comparison with Real Data
SN - 978-989-758-396-4
AU - Thiongane M.
AU - Chan W.
AU - L’Ecuyer P.
PY - 2020
SP - 100
EP - 108
DO - 10.5220/0009181401000108