Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2022
Abstract
This paper concerns the staffing optimization problem in multi-skill call centers. The objective is to find a minimal cost staffing solution while meeting a target level for the quality of service (QoS) to customers. We consider a staffing problem in which joint chance constraints are imposed on the QoS of the day. Our joint chance-constrained formulation is more rational capturing the correlation between different call types, as compared to separate chance-constrained versions considered in previous studies. We show that, in general, the probability functions in the joint-chance constraints display S-shaped curves, and the optimal solutions should belong to the concave regions of the curves. Thus, we propose an approach combining a heuristic phase to identify solutions lying in the concave part and a simulation-based cut generation phase to create outer-approximations of the probability functions. This allows us to find good staffing solutions satisfying the joint-chance constraints by simulation and linear programming. We test our formulation and algorithm using call center examples of up to 65 call types and 89 agent groups, which shows the benefits of our joint-chance constrained formulation and the advantage of our algorithm over standard ones.
Keywords
Call center, Staffing optimization, Joint chance constraint, Cutting plane, Concave-identification
Discipline
Artificial Intelligence and Robotics | Operations and Supply Chain Management | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Journal of Combinatorial Optimization
Volume
44
Issue
1
First Page
354
Last Page
378
ISSN
1382-6905
Identifier
10.1007/s10878-021-00830-1
Publisher
Springer
Citation
DAM, Tien Thanh; TA, Thuy Anh; and MAI, Tien.
Joint chance-constrained staffing optimization in multi-skill call centers. (2022). Journal of Combinatorial Optimization. 44, (1), 354-378.
Available at: https://ink.library.smu.edu.sg/sis_research/6954
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/10.1007/s10878-021-00830-1
Included in
Artificial Intelligence and Robotics Commons, Operations and Supply Chain Management Commons, Operations Research, Systems Engineering and Industrial Engineering Commons