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Dynamic Scheduling of a Multiclass Fluid Network

Author

Listed:
  • Hong Chen

    (University of British Columbia, Vancouver, Canada and New Jersey Institute of Technology, Newark, New Jersey)

  • David D. Yao

    (Columbia University, New York, New York)

Abstract

A fluid network is a deterministic network model in which dynamic continuous flows are circulated and processed among a set of stations. A fluid network often describes the asymptotic behavior of a stochastic queueing network via functional strong law of large numbers. We study the dynamic scheduling of multiple classes of fluid traffic in such a network. An algorithm is developed that systematically solves the dynamic scheduling problem by solving a sequence of linear programs. It generates a policy, in the form of dynamic capacity allocation at each station (among all fluid classes), that consists of a finite set of linear “pieces” over the entire time horizon. In a single-station, or equivalently, single-server, network, this solution procedure recovers the priority index set that is optimal for the corresponding discrete queueing model, generally known as Klimov's problem.

Suggested Citation

  • Hong Chen & David D. Yao, 1993. "Dynamic Scheduling of a Multiclass Fluid Network," Operations Research, INFORMS, vol. 41(6), pages 1104-1115, December.
    • Handle: RePEc:inm:oropre:v:41:y:1993:i:6:p:1104-1115
    DOI: 10.1287/opre.41.6.1104
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    Citations

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    Cited by:

    1. Yoni Nazarathy & Gideon Weiss, 2009. "Near optimal control of queueing networks over a finite time horizon," Annals of Operations Research, Springer, vol. 170(1), pages 233-249, September.
    2. Yang, Jun & Yan, Houmin & Sethi, Suresh P., 1999. "Optimal production planning in pull flow lines with multiple products," European Journal of Operational Research, Elsevier, vol. 119(3), pages 582-604, December.
    3. Dragos Florin Ciocan & Vivek Farias, 2012. "Model Predictive Control for Dynamic Resource Allocation," Mathematics of Operations Research, INFORMS, vol. 37(3), pages 501-525, August.
    4. Constantinos Maglaras, 2006. "Revenue Management for a Multiclass Single-Server Queue via a Fluid Model Analysis," Operations Research, INFORMS, vol. 54(5), pages 914-932, October.
    5. Noa Zychlinski, 2023. "Applications of fluid models in service operations management," Queueing Systems: Theory and Applications, Springer, vol. 103(1), pages 161-185, February.
    6. J. G. Dai & Gideon Weiss, 2002. "A Fluid Heuristic for Minimizing Makespan in Job Shops," Operations Research, INFORMS, vol. 50(4), pages 692-707, August.
    7. Junfei Huang & Boaz Carmeli & Avishai Mandelbaum, 2015. "Control of Patient Flow in Emergency Departments, or Multiclass Queues with Deadlines and Feedback," Operations Research, INFORMS, vol. 63(4), pages 892-908, August.
    8. Dimitris Bertsimas & David Gamarnik & Jay Sethuraman, 2003. "From Fluid Relaxations to Practical Algorithms for High-Multiplicity Job-Shop Scheduling: The Holding Cost Objective," Operations Research, INFORMS, vol. 51(5), pages 798-813, October.
    9. Maglaras, Constantinos & Van Mieghem, Jan A., 2005. "Queueing systems with leadtime constraints: A fluid-model approach for admission and sequencing control," European Journal of Operational Research, Elsevier, vol. 167(1), pages 179-207, November.
    10. J. G. Dai & Wuqin Lin, 2005. "Maximum Pressure Policies in Stochastic Processing Networks," Operations Research, INFORMS, vol. 53(2), pages 197-218, April.

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