On a data-driven method for staffing large call centers

Achal Bassamboo*, Assaf Zeevi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)714-726
Number of pages13
JournalOperations Research
Volume57
Issue number3
DOIs
StatePublished - May 1 2009

Keywords

  • Approximations
  • Limit theorem
  • Nonstationary queues
  • Stochastic model applications
  • Stochastic networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'On a data-driven method for staffing large call centers'. Together they form a unique fingerprint.

Cite this