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OM Forum —Offered Load Analysis for Staffing

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  • Ward Whitt

    (Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027)

Abstract

This essay, based on my 2012 MSOM Fellow Lecture, discusses an idea that has been useful for developing effective methods to set staffing levels in service systems: offered load analysis. The main idea is to tackle a hard problem by first seeking an insightful simplification. For capacity planning to meet uncertain exogenous demand, offered load analysis looks at the amount of capacity that would be used if there were no constraints on its availability. This simplification is helpful because the stochastic model becomes much more tractable. Offered load analysis can be especially helpful when the demand is not only uncertain but also time varying, as in many service systems. Given the distribution of the stochastic offered load, we often can set staffing levels to stabilize performance at target levels, even in face of a strongly time-varying arrival rate, long service times, and network structure.

Suggested Citation

  • Ward Whitt, 2013. "OM Forum —Offered Load Analysis for Staffing," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 166-169, May.
    • Handle: RePEc:inm:ormsom:v:15:y:2013:i:2:p:166-169
    DOI: 10.1287/msom.1120.0428
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    References listed on IDEAS

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    1. Yunan Liu & Ward Whitt, 2012. "Stabilizing Customer Abandonment in Many-Server Queues with Time-Varying Arrivals," Operations Research, INFORMS, vol. 60(6), pages 1551-1564, December.
    2. Wallace J. Hopp & Mark L. Spearman, 2004. "To Pull or Not to Pull: What Is the Question?," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 133-148, August.
    3. Gérard P. Cachon, 2012. "What Is Interesting in Operations Management?," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 166-169, April.
    4. 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.
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    Cited by:

    1. Liu, Yunan & Whitt, Ward, 2017. "Stabilizing performance in a service system with time-varying arrivals and customer feedback," European Journal of Operational Research, Elsevier, vol. 256(2), pages 473-486.
    2. Vijayalakshmi Chetlapalli & K. S. S. Iyer & Himanshu Agrawal, 2020. "Modelling time-dependent aggregate traffic in 5G networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 73(4), pages 557-575, April.
    3. Na Li & Xiaorui Li & Paul Forero, 2022. "Physician scheduling for outpatient department with nonhomogeneous patient arrival and priority queue," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 879-915, December.
    4. Noa Zychlinski & Avishai Mandelbaum & Petar Momčilović, 2018. "Time-varying tandem queues with blocking: modeling, analysis, and operational insights via fluid models with reflection," Queueing Systems: Theory and Applications, Springer, vol. 89(1), pages 15-47, June.
    5. Galit B. Yom-Tov & Avishai Mandelbaum, 2014. "Erlang-R: A Time-Varying Queue with Reentrant Customers, in Support of Healthcare Staffing," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 283-299, May.
    6. Opher Baron & Oded Berman & Dmitry Krass & Jianfu Wang, 2017. "Strategic Idleness and Dynamic Scheduling in an Open-Shop Service Network: Case Study and Analysis," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 52-71, February.
    7. Ward Whitt & Jingtong Zhao, 2017. "Many‐server loss models with non‐poisson time‐varying arrivals," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(3), pages 177-202, April.

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