Partial cross training in call centers with uncertain arrivals and global service level agreements
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Speeding up call center simulation and optimization by Markov chain uniformization
Proceedings of the 40th Conference on Winter Simulation
On a Data-Driven Method for Staffing Large Call Centers
Operations Research
Solving a stochastic queueing design and control problem with constraint programming
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A constraint programming approach for solving a queueing control problem
Journal of Artificial Intelligence Research
Staffing optimization in complex service delivery systems
Proceedings of the 7th International Conference on Network and Services Management
Variance bounds and existence results for randomly shifted lattice rules
Journal of Computational and Applied Mathematics
Modeling a complex global service delivery system
Proceedings of the Winter Simulation Conference
Discrete-valued, stochastic-constrained simulation optimization with compass
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Computers and Industrial Engineering
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We study an iterative cutting-plane algorithm on an integer program for minimizing the staffing costs of a multiskill call center subject to service-level requirements that are estimated by simulation. We solve a sample average version of the problem, where the service levels are expressed as functions of the staffing for a fixed sequence of random numbers driving the simulation. An optimal solution of this sample problem is also an optimal solution to the original problem when the sample size is large enough. Several difficulties are encountered when solving the sample problem, especially for large problem instances, and we propose practical heuristics to deal with these difficulties. We report numerical experiments with examples of different sizes. The largest example corresponds to a real-life call center with 65 types of calls and 89 types of agents (skill groups).