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Approximations for the Queue Length Distributions of Time-Varying Many-Server Queues

Author

Listed:
  • Jamol Pender

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

  • Young Myoung Ko

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, 37673, Korea)

Abstract

This paper presents a novel and computationally efficient methodology for approximating the queue length (the number of customers in the system) distributions of time-varying non-Markovian many-server queues (e.g., G t / G t / n t queues), where the number of servers ( n t ) is large. Our methodology consists of two steps. The first step uses phase-type distributions to approximate the general interarrival and service times, thus generating an approximating Ph t / Ph t / n t queue. The second step develops strong approximation theory to approximate the Ph t / Ph t / n t queue with fluid and diffusion limits whose mean and variance can be computed using ordinary differential equations. However, by naively representing the Ph t / Ph t / n t queue as a Markov process by expanding the state space, we encounter the lingering phenomenon even when the queue is overloaded . Lingering typically occurs when the mean queue length is equal or near the number of servers, however, in this case it also happens when the queue is overloaded and this time is not of zero measure. As a result, we develop an alternative representation for the queue length process that avoids the lingering problem in the overloaded case, thus allowing for the derivation of a Gaussian diffusion limit. Finally, we compare the effectiveness of our proposed method with discrete event simulation in a variety parameter settings and show that our approximations are very accurate.

Suggested Citation

  • Jamol Pender & Young Myoung Ko, 2017. "Approximations for the Queue Length Distributions of Time-Varying Many-Server Queues," INFORMS Journal on Computing, INFORMS, vol. 29(4), pages 688-704, November.
  • Handle: RePEc:inm:orijoc:v:29:y:2017:i:4:p:688-704
    DOI: 10.1287/ijoc.2017.0760
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    References listed on IDEAS

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    1. Kaiqi Yu & Mei-Ling Huang & Percy H. Brill, 2012. "An Algorithm for Fitting Heavy-Tailed Distributions via Generalized Hyperexponentials," INFORMS Journal on Computing, INFORMS, vol. 24(1), pages 42-52, February.
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    4. 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.
    5. Young Myoung Ko & Natarajan Gautam, 2013. "Critically Loaded Time-Varying Multiserver Queues: Computational Challenges and Approximations," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 285-301, May.
    6. Ward Whitt, 2006. "Fluid Models for Multiserver Queues with Abandonments," Operations Research, INFORMS, vol. 54(1), pages 37-54, February.
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    Cited by:

    1. Yiran Liu & Harsha Honnappa & Samy Tindel & Nung Kwan Yip, 2021. "Infinite server queues in a random fast oscillatory environment," Queueing Systems: Theory and Applications, Springer, vol. 98(1), pages 145-179, June.
    2. Ryan Palmer & Martin Utley, 2020. "On the modelling and performance measurement of service networks with heterogeneous customers," Annals of Operations Research, Springer, vol. 293(1), pages 237-268, October.
    3. 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.
    4. William A. Massey & Jamol Pender, 2018. "Dynamic rate Erlang-A queues," Queueing Systems: Theory and Applications, Springer, vol. 89(1), pages 127-164, June.

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