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A Method for Staffing Large Call Centers Based on Stochastic Fluid Models

A Method for Staffing Large Call Centers Based on Stochastic Fluid Models We consider a call center model with m input flows and r pools of agents; the m-vector λ of instantaneous arrival rates is allowed to be time dependent and to vary stochastically. Seeking to optimize the trade-off between personnel costs and abandonment penalties, we develop and illustrate a practical method for sizing the r agent pools. Using stochastic fluid models, this method reduces the staffing problem to a multidimensional newsvendor problem, which can be solved numerically by a combination of linear programming and Monte Carlo simulation. Numerical examples are presented, and in all cases the pool sizes derived by means of the proposed method are very close to optimal. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Manufacturing & Service Operations Management INFORMS

A Method for Staffing Large Call Centers Based on Stochastic Fluid Models

17 pages

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References (22)

Publisher
INFORMS
Copyright
Copyright © INFORMS
Subject
Research Article
ISSN
1523-4614
eISSN
1526-5498
DOI
10.1287/msom.1040.0052
Publisher site
See Article on Publisher Site

Abstract

We consider a call center model with m input flows and r pools of agents; the m-vector λ of instantaneous arrival rates is allowed to be time dependent and to vary stochastically. Seeking to optimize the trade-off between personnel costs and abandonment penalties, we develop and illustrate a practical method for sizing the r agent pools. Using stochastic fluid models, this method reduces the staffing problem to a multidimensional newsvendor problem, which can be solved numerically by a combination of linear programming and Monte Carlo simulation. Numerical examples are presented, and in all cases the pool sizes derived by means of the proposed method are very close to optimal.

Journal

Manufacturing & Service Operations ManagementINFORMS

Published: Jan 25, 2005

Keywords: Keywords : capacity sizing ; call centers ; fluid analysis ; multidimensional newsvendor ; nonstationarity ; queueing ; random environment ; stochastic programming

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