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

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/WSC57314.2022.10015281

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