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Service Interruptions in Large-Scale Service Systems

  • Guodong Pang


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

  • Ward Whitt


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

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    Large-scale service systems, where many servers respond to high demand, are appealing because they can provide great economy of scale, producing a high quality of service with high efficiency. Customer waiting times can be short, with a majority of customers served immediately upon arrival, while server utilizations remain close to 100%. However, we show that this confluence of quality and efficiency is not achieved without risk, because there can be severe congestion if the system does not operate as planned. In particular, we show that the large scale makes the system more vulnerable to service interruptions when (i) most customers remain waiting until they can be served, and (ii) when many servers are unable to function during the interruption, as may occur with a system-wide computer failure. Increasing scale leads to higher server utilizations, which in turn leads to longer recovery times from service interruptions and worse performance during such events. We quantify the impact of service interruptions with increasing scale by introducing and analyzing approximating deterministic fluid models. We also show that these fluid models can be obtained from many-server heavy-traffic limits.

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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 55 (2009)
    Issue (Month): 9 (September)
    Pages: 1499-1512

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    Handle: RePEc:inm:ormnsc:v:55:y:2009:i:9:p:1499-1512
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    1. Ward Whitt, 1992. "Understanding the Efficiency of Multi-Server Service Systems," Management Science, INFORMS, vol. 38(5), pages 708-723, May.
    2. Zohar Feldman & Avishai Mandelbaum & William A. Massey & Ward Whitt, 2008. "Staffing of Time-Varying Queues to Achieve Time-Stable Performance," Management Science, INFORMS, vol. 54(2), pages 324-338, February.
    3. William C. Jordan & Stephen C. Graves, 1995. "Principles on the Benefits of Manufacturing Process Flexibility," Management Science, INFORMS, vol. 41(4), pages 577-594, April.
    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. Atul Bhandari & Alan Scheller-Wolf & Mor Harchol-Balter, 2008. "An Exact and Efficient Algorithm for the Constrained Dynamic Operator Staffing Problem for Call Centers," Management Science, INFORMS, vol. 54(2), pages 339-353, February.
    6. 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.
    7. Rouba Ibrahim & Ward Whitt, 2009. "Real-Time Delay Estimation in Overloaded Multiserver Queues with Abandonments," Management Science, INFORMS, vol. 55(10), pages 1729-1742, October.
    8. Ohad Perry & Ward Whitt, 2009. "Responding to Unexpected Overloads in Large-Scale Service Systems," Management Science, INFORMS, vol. 55(8), pages 1353-1367, August.
    9. Ward Whitt, 2004. "Efficiency-Driven Heavy-Traffic Approximations for Many-Server Queues with Abandonments," Management Science, INFORMS, vol. 50(10), pages 1449-1461, October.
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