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Capacity Sizing Under Parameter Uncertainty: Safety Staffing Principles Revisited

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Author Info

  • Achal Bassamboo

    ()
    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

  • Ramandeep S. Randhawa

    ()
    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Assaf Zeevi

    ()
    (Graduate School of Business, Columbia University, New York, New York 10027)

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    Abstract

    We study a capacity sizing problem in a service system that is modeled as a single-class queue with multiple servers and where customers may renege while waiting for service. A salient feature of the model is that the mean arrival rate of work is random (in practice this is a typical consequence of forecasting errors). The paper elucidates the impact of uncertainty on the nature of capacity prescriptions, and relates these to well established rules-of-thumb such as the square-root safety staffing principle. We establish a simple and intuitive relationship between the incoming load (measured in Erlangs) and the extent of uncertainty in arrival rates (measured via the coefficient of variation) that characterizes the extent to which uncertainty dominates stochastic variability or vice versa. In the former case it is shown that traditional square-root safety staffing logic is no longer valid, yet simple capacity prescriptions derived via a suitable newsvendor problem are surprisingly accurate.

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    File URL: http://dx.doi.org/10.1287/mnsc.1100.1203
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 56 (2010)
    Issue (Month): 10 (October)
    Pages: 1668-1686

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    Handle: RePEc:inm:ormnsc:v:56:y:2010:i:10:p:1668-1686

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    Related research

    Keywords: service systems; capacity sizing; parameter uncertainty;

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    Cited by:
    1. Hu, Xiangling & Motwani, Jaideep G., 2014. "Minimizing downside risks for global sourcing under price-sensitive stochastic demand, exchange rate uncertainties, and supplier capacity constraints," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 398-409.

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