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What you should know about queueing models to set staffing requirements in service systems

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  • Ward Whitt

Abstract

One traditional application of queueing models is to help set staffing requirements in service systems, but the way to do so is not entirely straightforward, largely because demand in service systems typically varies greatly by the time of day. This article discusses ways—old and new—to cope with that time‐varying demand. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007

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  • Ward Whitt, 2007. "What you should know about queueing models to set staffing requirements in service systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(5), pages 476-484, August.
    • Handle: RePEc:wly:navres:v:54:y:2007:i:5:p:476-484
    DOI: 10.1002/nav.20243
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    1. Linda Green & Peter Kolesar, 1991. "The Pointwise Stationary Approximation for Queues with Nonstationary Arrivals," Management Science, INFORMS, vol. 37(1), pages 84-97, January.
    2. Noah Gans & Ger Koole & Avishai Mandelbaum, 2003. "Telephone Call Centers: Tutorial, Review, and Research Prospects," Manufacturing & Service Operations Management, INFORMS, vol. 5(2), pages 79-141, September.
    3. 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.
    4. Rodney B. Wallace & Ward Whitt, 2005. "A Staffing Algorithm for Call Centers with Skill-Based Routing," Manufacturing & Service Operations Management, INFORMS, vol. 7(4), pages 276-294, August.
    5. Lawrence Brown & Noah Gans & Avishai Mandelbaum & Anat Sakov & Haipeng Shen & Sergey Zeltyn & Linda Zhao, 2005. "Statistical Analysis of a Telephone Call Center: A Queueing-Science Perspective," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 36-50, March.
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    Cited by:

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    2. Saif Benjaafar & Shining Wu & Hanlin Liu & Einar Bjarki Gunnarsson, 2022. "Dimensioning On-Demand Vehicle Sharing Systems," Management Science, INFORMS, vol. 68(2), pages 1218-1232, February.
    3. Shone, Rob & Glazebrook, Kevin & Zografos, Konstantinos G., 2019. "Resource allocation in congested queueing systems with time-varying demand: An application to airport operations," European Journal of Operational Research, Elsevier, vol. 276(2), pages 566-581.
    4. 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.
    5. Ran Liu & Xiaolan Xie, 2018. "Physician Staffing for Emergency Departments with Time-Varying Demand," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 588-607, August.
    6. Noa Zychlinski & Avishai Mandelbaum & Petar Momčilović & Izack Cohen, 2020. "Bed Blocking in Hospitals Due to Scarce Capacity in Geriatric Institutions—Cost Minimization via Fluid Models," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 396-411, March.
    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.
    8. Ionut NICA, 2020. "Operational Research in the Emergency Medical System of Romania," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 11(1), pages 3-13.
    9. Gang Li & Joy M. Field & Hongxun Jiang & Tian He & Youming Pang, 2019. "Decision Models for Workforce and Technology Planning in Services," Papers 1909.12829, arXiv.org.
    10. Tevfik Aktekin & Tahir Ekin, 2016. "Stochastic call center staffing with uncertain arrival, service and abandonment rates: A Bayesian perspective," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(6), pages 460-478, September.

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