IDEAS home Printed from https://ideas.repec.org/a/spr/queues/v82y2016i1d10.1007_s11134-015-9456-8.html
   My bibliography  Save this article

The analysis of cyclic stochastic fluid flows with time-varying transition rates

Author

Listed:
  • Barbara Margolius

    (Cleveland State University)

  • Małgorzata M. O’Reilly

    (University of Tasmania)

Abstract

We consider a stochastic fluid model (SFM) $$\{(\widehat{X}(t),J(t)),t\ge 0\}$$ { ( X ^ ( t ) , J ( t ) ) , t ≥ 0 } driven by a continuous-time Markov chain $$\{J(t),t\ge 0 \}$$ { J ( t ) , t ≥ 0 } with a time-varying generator $$T(t)$$ T ( t ) and cycle of length 1 such that $$T(t)=T(t+1)$$ T ( t ) = T ( t + 1 ) for all $$t\ge 0$$ t ≥ 0 . We derive theoretical expressions for the key periodic measures for the analysis of the model, and develop efficient methods for their numerical computation. We illustrate the theory with numerical examples. This work is an extension of the results in Bean et al. (Stoch. Models 21(1):149–184, 2005) for a standard SFM with time-homogeneous generator, and suggests a possible alternative approach to that developed by Yunan and Whitt (Queueing Syst. 71(4):405–444, 2012).

Suggested Citation

  • Barbara Margolius & Małgorzata M. O’Reilly, 2016. "The analysis of cyclic stochastic fluid flows with time-varying transition rates," Queueing Systems: Theory and Applications, Springer, vol. 82(1), pages 43-73, February.
    • Handle: RePEc:spr:queues:v:82:y:2016:i:1:d:10.1007_s11134-015-9456-8
    DOI: 10.1007/s11134-015-9456-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11134-015-9456-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11134-015-9456-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yunan Liu & Ward Whitt, 2011. "A Network of Time-Varying Many-Server Fluid Queues with Customer Abandonment," Operations Research, INFORMS, vol. 59(4), pages 835-846, August.
    2. Yunan Liu & Ward Whitt, 2014. "Algorithms for Time-Varying Networks of Many-Server Fluid Queues," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 59-73, February.
    3. Bean, Nigel G. & O'Reilly, Malgorzata M. & Taylor, Peter G., 2005. "Hitting probabilities and hitting times for stochastic fluid flows," Stochastic Processes and their Applications, Elsevier, vol. 115(9), pages 1530-1556, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nikki Sonenberg & Peter G. Taylor, 2019. "Networks of interacting stochastic fluid models with infinite and finite buffers," Queueing Systems: Theory and Applications, Springer, vol. 92(3), pages 293-322, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Max Tschaikowski & Mirco Tribastone, 2017. "A computational approach to steady-state convergence of fluid limits for Coxian queuing networks with abandonment," Annals of Operations Research, Springer, vol. 252(1), pages 101-120, May.
    2. Kawai, Yosuke & Takagi, Hideaki, 2015. "Fluid approximation analysis of a call center model with time-varying arrivals and after-call work," Operations Research Perspectives, Elsevier, vol. 2(C), pages 81-96.
    3. Avishai Mandelbaum & Petar Momčilović, 2017. "Personalized queues: the customer view, via a fluid model of serving least-patient first," Queueing Systems: Theory and Applications, Springer, vol. 87(1), pages 23-53, October.
    4. Jonathan E. Helm & Mark P. Van Oyen, 2014. "Design and Optimization Methods for Elective Hospital Admissions," Operations Research, INFORMS, vol. 62(6), pages 1265-1282, December.
    5. Bean, Nigel G. & Nguyen, Giang T. & Nielsen, Bo F. & Peralta, Oscar, 2022. "RAP-modulated fluid processes: First passages and the stationary distribution," Stochastic Processes and their Applications, Elsevier, vol. 149(C), pages 308-340.
    6. Yongjiang Guo & Yunan Liu & Renhu Pei, 2018. "Functional law of the iterated logarithm for multi-server queues with batch arrivals and customer feedback," Annals of Operations Research, Springer, vol. 264(1), pages 157-191, May.
    7. Mozhu Wang & Jianming Yao, 2023. "A reliable location design of unmanned vending machines based on customer satisfaction," Electronic Commerce Research, Springer, vol. 23(1), pages 541-575, March.
    8. Defraeye, Mieke & Van Nieuwenhuyse, Inneke, 2016. "Staffing and scheduling under nonstationary demand for service: A literature review," Omega, Elsevier, vol. 58(C), pages 4-25.
    9. Hu, Lu & Zhao, Bin & Zhu, Juanxiu & Jiang, Yangsheng, 2019. "Two time-varying and state-dependent fluid queuing models for traffic circulation systems," European Journal of Operational Research, Elsevier, vol. 275(3), pages 997-1019.
    10. Noa Zychlinski, 2023. "Applications of fluid models in service operations management," Queueing Systems: Theory and Applications, Springer, vol. 103(1), pages 161-185, February.
    11. Ahn, Soohan & Badescu, Andrei L. & Cheung, Eric C.K. & Kim, Jeong-Rae, 2018. "An IBNR–RBNS insurance risk model with marked Poisson arrivals," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 26-42.
    12. 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.
    13. O’Reilly, Małgorzata M., 2014. "Multi-stage stochastic fluid models for congestion control," European Journal of Operational Research, Elsevier, vol. 238(2), pages 514-526.
    14. Nikki Sonenberg & Peter G. Taylor, 2019. "Networks of interacting stochastic fluid models with infinite and finite buffers," Queueing Systems: Theory and Applications, Springer, vol. 92(3), pages 293-322, August.
    15. Bean, Nigel G. & O’Reilly, Małgorzata M., 2014. "The stochastic fluid–fluid model: A stochastic fluid model driven by an uncountable-state process, which is a stochastic fluid model itself," Stochastic Processes and their Applications, Elsevier, vol. 124(5), pages 1741-1772.
    16. A. Korhan Aras & Xinyun Chen & Yunan Liu, 2018. "Many-server Gaussian limits for overloaded non-Markovian queues with customer abandonment," Queueing Systems: Theory and Applications, Springer, vol. 89(1), pages 81-125, June.
    17. Samuelson, Aviva & Haigh, Andrew & O'Reilly, Małgorzata M. & Bean, Nigel G., 2017. "Stochastic model for maintenance in continuously deteriorating systems," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1169-1179.
    18. 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.
    19. Avishai Mandelbaum & Petar Momčilović & Nikolaos Trichakis & Sarah Kadish & Ryan Leib & Craig A. Bunnell, 2020. "Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center," Management Science, INFORMS, vol. 66(1), pages 243-270, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:queues:v:82:y:2016:i:1:d:10.1007_s11134-015-9456-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.