Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2022
Abstract
We study a staffing optimization problem in multi-skill call centers. The objective is to minimize the total cost of agents under some quality of service (QoS) constraints. The key challenge lies in the fact that the QoS functions have no closed-form and need to be approximated by simulation. In this paper we propose a new way to approximate the QoS functions by logistic functions and design a new algorithm that combines logistic regression, cut generations and logistic-based local search to efficiently find good staffing solutions. We report computational results using examples up to 65 call types and 89 agent groups showing that our approach performs well in practice, in terms of solution quality and computing time.
Keywords
logistic regression, simulation, call center, cutting plane
Discipline
Artificial Intelligence and Robotics | Programming Languages and Compilers
Research Areas
Intelligent Systems and Optimization
Publication
2022 Winter Simulation Conference: Singapore, December 11-14: Proceedings
First Page
1
Last Page
12
ISBN
9781665476614
Identifier
10.1109/WSC57314.2022.10015281
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
TA, Thuy Anh; MAI, Tien; BASTIN, Fabian; and l'ECUYER, Pierre.
A logistic regression and linear programming approach for multi-skill staffing optimization in call centers. (2022). 2022 Winter Simulation Conference: Singapore, December 11-14: Proceedings. 1-12.
Available at: https://ink.library.smu.edu.sg/sis_research/7308
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/WSC57314.2022.10015281
Included in
Artificial Intelligence and Robotics Commons, Programming Languages and Compilers Commons