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Service Interruptions in Large-Scale Service Systems

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • Guodong Pang & Ward Whitt, 2009. "Service Interruptions in Large-Scale Service Systems," Management Science, INFORMS, vol. 55(9), pages 1499-1512, September.
    • Handle: RePEc:inm:ormnsc:v:55:y:2009:i:9:p:1499-1512
    DOI: 10.1287/mnsc.1090.1038
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    References listed on IDEAS

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    1. Shlomo Halfin & Ward Whitt, 1981. "Heavy-Traffic Limits for Queues with Many Exponential Servers," Operations Research, INFORMS, vol. 29(3), pages 567-588, June.
    2. 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.
    3. 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.
    4. Ward Whitt, 2004. "Efficiency-Driven Heavy-Traffic Approximations for Many-Server Queues with Abandonments," Management Science, INFORMS, vol. 50(10), pages 1449-1461, October.
    5. 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.
    6. 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.
    7. Ward Whitt, 1992. "Understanding the Efficiency of Multi-Server Service Systems," Management Science, INFORMS, vol. 38(5), pages 708-723, May.
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    10. Ohad Perry & Ward Whitt, 2009. "Responding to Unexpected Overloads in Large-Scale Service Systems," Management Science, INFORMS, vol. 55(8), pages 1353-1367, August.
    11. Hong Chen & David D. Yao, 1992. "A Fluid Model for Systems with Random Disruptions," Operations Research, INFORMS, vol. 40(3-supplem), pages 239-247, June.
    12. Mor Armony & Nahum Shimkin & Ward Whitt, 2009. "The Impact of Delay Announcements in Many-Server Queues with Abandonment," Operations Research, INFORMS, vol. 57(1), pages 66-81, February.
    13. 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.
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    Cited by:

    1. Zhang, Zhe George & Kim, Ilhyung & Springer, Mark & Cai, Gangshu (George) & Yu, Yugang, 2013. "Dynamic pooling of make-to-stock and make-to-order operations," International Journal of Production Economics, Elsevier, vol. 144(1), pages 44-56.
    2. Feng Dong & Qiguo Gong & Hui Wang & Siyuan Yi & Yue Cai, 2019. "Rest Breaks Arrange Based on Empirical Studies of Productivity in Manufacturing Industry," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(9), pages 1043-1066, September.
    3. Bo Zhang & Bert Zwart, 2013. "Steady-State Analysis for Multiserver Queues Under Size Interval Task Assignment in the Quality-Driven Regime," Mathematics of Operations Research, INFORMS, vol. 38(3), pages 504-525, August.
    4. Limin Rong & Feng Dong & Qiguo Gong, 2021. "Work Interruption: The Moderate Effect of Workload and Queue Length in the Manufacturing Industry," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 11(8), pages 618-631, August.

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