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Adaptive server staffing in the presence of time-varying arrivals: a feed-forward control approach

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  • M C Testik

    (Cukurova University)

  • J K Cochran

    (Arizona State University)

  • G C Runger

    (Arizona State University)

Abstract

We study the short-term staffing problem of systems that experience random, non-stationary demand. The typical method to accommodate changes in arrival rate is to use historical data to identify peak periods and associated forecasting for upcoming time windows. In this paper, we develop a method that instead detects change as it happens. Motivated by an automatic call distributor system in a call centre with time-varying arrivals, we propose a change detection algorithm based upon non-homogeneous Poisson processes. The proposed method is general and may be thought of as a feed-forward strategy, in which we detect a change in the arrival process, estimate the new magnitude of the arrival rate, and assign an appropriate number of servers to the tasks. The generalized likelihood ratio statistic is used and a recommendation for its decision limit is developed. Simulation results are used to evaluate the performance of the detector in the context of a telephone call centre.

Suggested Citation

  • M C Testik & J K Cochran & G C Runger, 2004. "Adaptive server staffing in the presence of time-varying arrivals: a feed-forward control approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(3), pages 233-239, March.
    • Handle: RePEc:pal:jorsoc:v:55:y:2004:i:3:d:10.1057_palgrave.jors.2601677
    DOI: 10.1057/palgrave.jors.2601677
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    References listed on IDEAS

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    1. Ingolfsson, Armann & Amanul Haque, Md. & Umnikov, Alex, 2002. "Accounting for time-varying queueing effects in workforce scheduling," European Journal of Operational Research, Elsevier, vol. 139(3), pages 585-597, June.
    2. Otis B. Jennings & Avishai Mandelbaum & William A. Massey & Ward Whitt, 1996. "Server Staffing to Meet Time-Varying Demand," Management Science, INFORMS, vol. 42(10), pages 1383-1394, October.
    3. Geurt Jongbloed & Ger Koole, 2001. "Managing uncertainty in call centres using Poisson mixtures," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(4), pages 307-318, October.
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    Cited by:

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    3. Ira Gerhardt & Barry L. Nelson, 2009. "Transforming Renewal Processes for Simulation of Nonstationary Arrival Processes," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 630-640, November.
    4. Santos Marta & Morais Manuel Cabral & Pacheco António, 2018. "Comparing Short-Memory Charts to Monitor the Traffic Intensity of Single Server Queues," Stochastics and Quality Control, De Gruyter, vol. 33(1), pages 1-21, June.

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