Design and Control of a Large Call Center: Asymptotic Analysis of an LP-Based Method

Achal Bassamboo (), J. Michael Harrison () and Assaf Zeevi ()
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Achal Bassamboo: Kellogg School of Management, Northwestern University, Evanston, Illinois 60208
J. Michael Harrison: Graduate School of Business, Stanford University, Stanford, California 94305
Assaf Zeevi: Graduate School of Business, Columbia University, New York, New York 10027

Operations Research, 2006, vol. 54, issue 3, 419-435

Abstract: This paper analyzes a call center model with m customer classes and r agent pools. The model is one with doubly stochastic arrivals, which means that the m -vector (lambda) of instantaneous arrival rates is allowed to vary both temporally and stochastically. Two levels of call center management are considered: staffing the r pools of agents, and dynamically routing calls to agents. The system manager’s objective is to minimize the sum of personnel costs and abandonment penalties. We consider a limiting parameter regime that is natural for call centers and relatively easy to analyze, but apparently novel in the literature of applied probability. For that parameter regime, we prove an asymptotic lower bound on expected total cost, which uses a strikingly simple distillation of the original system data. We then propose a method for staffing and routing based on linear programming (LP), and show that it achieves the asymptotic lower bound on expected total cost; in that sense the proposed method is asymptotically optimal .

Keywords: stochastic model applications; call centers; queueing; dynamic routing; fluid limits; doubly stochastic; asymptotic analysis; discrete review; performance bounds; abandonments; staffing (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (40)

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