Operations Research
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OPERATIONS RESEARCH
Vol. 52, No. 1, January-February 2004, pp. 17-34
DOI: 10.1287/opre.1030.0081
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Dimensioning Large Call Centers

Sem Borst, Avi Mandelbaum, Martin I. Reiman

CWI, P. O. Box 94079, 1090 GB Amsterdam, The Netherlands, and Bell Labs, Lucent Technologies, Murray Hill, New Jersey 07974-0636
Faculty of Industrial Engineering and Management, Technion, Haifa 32000, Israel
Bell Labs, Lucent Technologies, Murray Hill, New Jersey 07974-0636

sem.borst{at}cwi.nl
avim{at}tx.technion.ac.il
marty{at}research.bell-labs.com

We develop a framework for asymptotic optimization of a queueing system. The motivation is the staffing problem of large call centers, which we have modeled as M/M/N queues with N, the number of agents, being large. Within our framework, we determine the asymptotically optimal staffing level N* that trades off agents' costs with service quality: the higher the latter, the more expensive is the former. As an alternative to this optimization, we also develop a constraint satisfaction approach where one chooses the least N* that adheres to a given constraint on waiting cost. Either way, the analysis gives rise to three regimes of operation: quality-driven, where the focus is on service quality; efficiency-driven, which emphasizes agents' costs; and a rationalized regime that balances, and in fact unifies, the other two. Numerical experiments reveal remarkable accuracy of our asymptotic approximations: over a wide range of parameters, from the very small to the extremely large, N* is exactly optimal, or it is accurate to within a single agent. We demonstrate the utility of our approach by revisiting the square-root safety staffing principle, which is a long-existing rule of thumb for staffing the M/M/N queue. In its simplest form, our rule is as follows: if c is the hourly cost of an agent, and a is the hourly cost of customers' delay, then N* = R + y* (a/c){surd}R, where R is the offered load, and y*(·) is a function that is easily computable.

Subject classifications: Queues, optimization: choosing optimal number of servers; Queues, limit theorems: many server queues.
History: Received December 2000; revision received May 2002; accepted March 2003.




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