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On a Data-Driven Method for Staffing Large Call Centers

Kellogg School of Management, Northwestern University, Evanston, Illinois 60208
Graduate School of Business, Columbia University, New York, New York 10027
a-bassamboo{at}northwestern.edu
assaf{at}gsb.columbia.edu

We consider a call center model with multiple customer classes and multiple server pools. Calls arrive randomly over time, and the instantaneous arrival rates are allowed to vary both temporally and stochastically in an arbitrary manner. The objective is to minimize the sum of personnel costs and expected abandonment penalties by selecting an appropriate staffing level for each server pool. We propose a simple and computationally tractable method for solving this problem that requires as input only a few system parameters and historical call arrival data for each customer class; in this sense the method is said to be data-driven. The efficacy of the proposed method is illustrated via numerical examples. An asymptotic analysis establishes that the prescribed staffing levels achieve near-optimal performance and characterizes the magnitude of the optimality gap.

Subject classifications: stochastic model applications; stochastic networks; nonstationary queues; limit theorem; approximations.
History: Received May 2006; revision received February 2008; accepted March 2008.

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