IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v34y2006i4p406-416.html
   My bibliography  Save this article

Near-optimal control policy for loss networks

Author

Listed:
  • Ku, Cheng-Yuan
  • Yen, David C.
  • Chang, I-Chiu
  • Huang, Shi-Ming
  • Jordan, Scott

Abstract

In this paper, the phenomenon of the optimal management of requests of service in general networks is formulated as a control problem for a finite number of multiserver loss queues with Markovian routing. This type of problem may arise in a wide range of fields, e.g., manufacturing industries, storage facilities, computer networks, and communication systems. Using inductive approach of dynamic programming, the optimal admission control can be induced to be the functions of the number of requested service in progress. However, for large-scale network, the computational burden to find optimal control policy may be infeasible due to its involvement of the states for all stations in the networks. Hence, the idea of bottleneck modeling is borrowed to compute the near-optimal admission control policy. We reduced the scale of loss network and decreased the difference between the original and reduced models by making compensation for system parameters. A novel method is proposed in this paper to compute the compensation. Numerical results show that the near-optimal control policy demonstrates close performance to the optimal policy.

Suggested Citation

  • Ku, Cheng-Yuan & Yen, David C. & Chang, I-Chiu & Huang, Shi-Ming & Jordan, Scott, 2006. "Near-optimal control policy for loss networks," Omega, Elsevier, vol. 34(4), pages 406-416, August.
    • Handle: RePEc:eee:jomega:v:34:y:2006:i:4:p:406-416
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305-0483(05)00003-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Koenigsberg, E, 1993. "Similarities and differences in cycling server queueing models," Omega, Elsevier, vol. 21(2), pages 163-173, March.
    2. Ku, Cheng-Yuan & Jordan, Scott, 2003. "Near optimal admission control for multiserver loss queues in series," European Journal of Operational Research, Elsevier, vol. 144(1), pages 166-178, January.
    3. S. P. Sethi & H. Yan & H. Zhang & Q. Zhang, 2002. "Optimal and Hierarchical Controls in Dynamic Stochastic Manufacturing Systems: A Survey," Manufacturing & Service Operations Management, INFORMS, vol. 4(2), pages 133-170.
    4. Shaler Stidham, 2002. "Analysis, Design, and Control of Queueing Systems," Operations Research, INFORMS, vol. 50(1), pages 197-216, February.
    5. Papadopoulos, H. T., 1996. "A field service support system using a queueing network model and the priority MVA algorithm," Omega, Elsevier, vol. 24(2), pages 195-203, April.
    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. Papier, Felix & Thonemann, Ulrich W., 2011. "Capacity rationing in rental systems with two customer classes and batch arrivals," Omega, Elsevier, vol. 39(1), pages 73-85, January.
    2. Alireza Pooya & Morteza Pakdaman, 2021. "A new continuous time optimal control model for manpower planning with promotion from inside the system," Operational Research, Springer, vol. 21(1), pages 349-364, March.

    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. Fajardo, Val Andrei & Drekic, Steve, 2015. "Controlling the workload of M/G/1 queues via the q-policy," European Journal of Operational Research, Elsevier, vol. 243(2), pages 607-617.
    2. Gabriel Zayas‐Cabán & Emmett J. Lodree & David L. Kaufman, 2020. "Optimal Control of Parallel Queues for Managing Volunteer Convergence," Production and Operations Management, Production and Operations Management Society, vol. 29(10), pages 2268-2288, October.
    3. Abhishek, & Legros, Benjamin & Fransoo, Jan C., 2021. "Performance evaluation of stochastic systems with dedicated delivery bays and general on-street parking," Other publications TiSEM 09ed9572-d59c-4f28-a9c4-b, Tilburg University, School of Economics and Management.
    4. Zou, Jing & Chang, Qing & Arinez, Jorge & Xiao, Guoxian, 2017. "Data-driven modeling and real-time distributed control for energy efficient manufacturing systems," Energy, Elsevier, vol. 127(C), pages 247-257.
    5. Legros, Benjamin, 2021. "Routing analyses for call centers with human and automated services," International Journal of Production Economics, Elsevier, vol. 240(C).
    6. Baric{s} Ata & Shiri Shneorson, 2006. "Dynamic Control of an M/M/1 Service System with Adjustable Arrival and Service Rates," Management Science, INFORMS, vol. 52(11), pages 1778-1791, November.
    7. Nasreddine Saadouli, 2021. "Stochastic programming model for production planning with stochastic aggregate demand and spreadsheet-based solution heuristics," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(4), pages 117-127.
    8. Rahimi-Ghahroodi, S. & Al Hanbali, A. & Vliegen, I.M.H. & Cohen, M.A., 2019. "Joint optimization of spare parts inventory and service engineers staffing with full backlogging," International Journal of Production Economics, Elsevier, vol. 212(C), pages 39-50.
    9. Chen, Shih-Pin, 2007. "Solving fuzzy queueing decision problems via a parametric mixed integer nonlinear programming method," European Journal of Operational Research, Elsevier, vol. 177(1), pages 445-457, February.
    10. Avi Herbon & Konstantin Kogan, 2014. "Time-dependent and independent control rules for coordinated production and pricing under demand uncertainty and finite planning horizons," Annals of Operations Research, Springer, vol. 223(1), pages 195-216, December.
    11. John D. C. Little, 2011. "OR FORUM---Little's Law as Viewed on Its 50th Anniversary," Operations Research, INFORMS, vol. 59(3), pages 536-549, June.
    12. Daniel F. Silva & Bo Zhang & Hayriye Ayhan, 2018. "Admission control strategies for tandem Markovian loss systems," Queueing Systems: Theory and Applications, Springer, vol. 90(1), pages 35-63, October.
    13. Phillip R. Jenkins & Matthew J. Robbins & Brian J. Lunday, 2018. "Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system," Annals of Operations Research, Springer, vol. 271(2), pages 641-678, December.
    14. Xinbo Zhang & Feng Zhang & Xiaohong Chen & Zhong Wan, 2014. "Polymorphic Uncertain Linear Programming for Generalized Production Planning Problems," Journal of Optimization, Hindawi, vol. 2014, pages 1-10, February.
    15. Vardar, Cem & Gel, Esma S. & Fowler, John W., 2007. "A framework for evaluating remote diagnostics investment decisions for semiconductor equipment suppliers," European Journal of Operational Research, Elsevier, vol. 180(3), pages 1411-1426, August.
    16. Kennedy, W. J. & Wayne Patterson, J. & Fredendall, Lawrence D., 2002. "An overview of recent literature on spare parts inventories," International Journal of Production Economics, Elsevier, vol. 76(2), pages 201-215, March.
    17. Fermín Mallor & Cristina Azcárate & Julio Barado, 2015. "Optimal control of ICU patient discharge: from theory to implementation," Health Care Management Science, Springer, vol. 18(3), pages 234-250, September.
    18. Huh, Woonghee Tim & Lee, Jaywon & Park, Heesang & Park, Kun Soo, 2019. "The potty parity problem: Towards gender equality at restrooms in business facilities," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    19. Eugene Khmelnitsky & Ernst Presman & Suresh Sethi, 2011. "Optimal production control of a failure-prone machine," Annals of Operations Research, Springer, vol. 182(1), pages 67-86, January.
    20. Lijian Lu & Xiaoming Yan, 2016. "Capacity investment decisions under risk aversion," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(3), pages 218-235, April.

    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:eee:jomega:v:34:y:2006:i:4:p:406-416. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    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.