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Transient behaviour of time-varying tandem queueing networks

Author

Listed:
  • Anjale Ramesh

    (University of Calicut)

  • M. Manoharan

    (University of Calicut)

Abstract

Most of the large-scale service systems in real life are subject to time-varying conditions, such as arrival rates, service rates, and other factors that can affect system performance. These systems can be adequately modelled using time-varying queueing systems, where one or several parameters change over time. Analysis of transient behaviour in such time-varying queueing systems is more challenging than their steady-state analysis. This study deals with the transient analysis of Markovian queues connected in tandem, where both the service and arrival processes at each station depend on time. To begin with, we derive the transient distributional relationship between the average workload and the customer’s waiting time in a single-server non-Markovian queue with time-varying arrival and service rates. We then generalise the transient laws for single-server queues to a k-station tandem network. Furthermore, we develop an algorithm for analysing transient performance measures in a k-station tandem queueing network and conduct a numerical study based on the algorithm. Numerical study supports the effectiveness of the algorithm, and the results provide insights into the transient behaviour of tandem networks, specifically in bottleneck scenarios. The study reveals that the location of the bottleneck station in a line has a significant impact on average workload in the stations.

Suggested Citation

  • Anjale Ramesh & M. Manoharan, 2025. "Transient behaviour of time-varying tandem queueing networks," OPSEARCH, Springer;Operational Research Society of India, vol. 62(1), pages 104-118, March.
    • Handle: RePEc:spr:opsear:v:62:y:2025:i:1:d:10.1007_s12597-024-00790-0
    DOI: 10.1007/s12597-024-00790-0
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    References listed on IDEAS

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    1. 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.
    2. Pengyi Shi & Mabel C. Chou & J. G. Dai & Ding Ding & Joe Sim, 2016. "Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time," Management Science, INFORMS, vol. 62(1), pages 1-28, January.
    3. N. U. Prabhu, 1967. "Transient Behaviour of a Tandem Queue," Management Science, INFORMS, vol. 13(9), pages 631-639, May.
    4. J. G. Dai & Pengyi Shi, 2017. "A Two-Time-Scale Approach to Time-Varying Queues in Hospital Inpatient Flow Management," Operations Research, INFORMS, vol. 65(2), pages 514-536, April.
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    Cited by:

    1. Anjale Ramesh & Manoharan M., 2025. "ML Estimation of Intensity Function in Non-homogeneous Poisson Processes," SN Operations Research Forum, Springer, vol. 6(2), pages 1-10, June.

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