On a data-driven method for staffing large call centers
Coauthor(s): Achal Bassamboo.
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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.
Source: Operations Research
Bassamboo, Achal, and Assaf Zeevi. "On a data-driven method for staffing large call centers." Operations Research 57, no. 3 (June 1, 2009): 417-726.