Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects
Manufacturing & Service Operations Management
Dimensioning Large Call Centers
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Mathematics of Operations Research
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Queueing Systems: Theory and Applications
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SIAM Journal on Optimization
Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach
Management Science
Service-Level Differentiation in Call Centers with Fully Flexible Servers
Management Science
Staffing Multiskill Call Centers via Linear Programming and Simulation
Management Science
Interday Forecasting and Intraday Updating of Call Center Arrivals
Manufacturing & Service Operations Management
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Mathematical Programming: Series A and B
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Mathematics of Operations Research
IPCO'10 Proceedings of the 14th international conference on Integer Programming and Combinatorial Optimization
Robust Design and Control of Call Centers with Flexible Interactive Voice Response Systems
Manufacturing & Service Operations Management
On the modeling and forecasting of call center arrivals
Proceedings of the Winter Simulation Conference
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We consider the problem of staffing call centers with multiple customer classes and agent types operating under quality-of-service (QoS) constraints and demand rate uncertainty. We introduce a formulation of the staffing problem that requires that the QoS constraints are met with high probability with respect to the uncertainty in the demand rate. We contrast this chance-constrained formulation with the average-performance constraints that have been used so far in the literature. We then propose a two-step solution for the staffing problem under chance constraints. In the first step, we introduce a random static planning problem (RSPP) and discuss how it can be solved using two different methods. The RSPP provides us with a first-order (or fluid) approximation for the true optimal staffing levels and a staffing frontier. In the second step, we solve a finite number of staffing problems with known arrival rates---the arrival rates on the optimal staffing frontier. Hence, our formulation and solution approach has the important property that it translates the problem with uncertain demand rates to one with known arrival rates. The output of our procedure is a solution that is feasible with respect to the chance constraint and nearly optimal for large call centers.