IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v229y2013i1p190-198.html
   My bibliography  Save this article

Accelerating the convergence of value iteration by using partial transition functions

Author

Listed:
  • Arruda, Edilson F.
  • Ourique, Fabrício O.
  • LaCombe, Jason
  • Almudevar, Anthony

Abstract

This work proposes an algorithm that makes use of partial information to improve the convergence properties of the value iteration algorithm in terms of the overall computational complexity. The algorithm iterates on a series of increasingly refined approximate models that converges to the true model according to an optimal linear rate, which coincides with the convergence rate of the original value iteration algorithm. The paper investigates the properties of the proposed algorithm and features a series of switchover queue examples which illustrates the efficacy of the method.

Suggested Citation

  • Arruda, Edilson F. & Ourique, Fabrício O. & LaCombe, Jason & Almudevar, Anthony, 2013. "Accelerating the convergence of value iteration by using partial transition functions," European Journal of Operational Research, Elsevier, vol. 229(1), pages 190-198.
    • Handle: RePEc:eee:ejores:v:229:y:2013:i:1:p:190-198
    DOI: 10.1016/j.ejor.2013.02.029
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221713001628
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2013.02.029?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. Arruda, E.F. & do Val, J.B.R., 2008. "Stability and optimality of a multi-product production and storage system under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 188(2), pages 406-427, July.
    2. Sauré, Antoine & Patrick, Jonathan & Tyldesley, Scott & Puterman, Martin L., 2012. "Dynamic multi-appointment patient scheduling for radiation therapy," European Journal of Operational Research, Elsevier, vol. 223(2), pages 573-584.
    3. Arruda, E.F. & Fragoso, M.D. & do Val, J.B.R., 2011. "Approximate dynamic programming via direct search in the space of value function approximations," European Journal of Operational Research, Elsevier, vol. 211(2), pages 343-351, June.
    4. Wanda Rosa-Hatko & Eldon Gunn, 1997. "Queues with switchover - A review and critique," Annals of Operations Research, Springer, vol. 69(0), pages 299-322, January.
    5. He, Miao & Zhao, Lei & Powell, Warren B., 2012. "Approximate dynamic programming algorithms for optimal dosage decisions in controlled ovarian hyperstimulation," European Journal of Operational Research, Elsevier, vol. 222(2), pages 328-340.
    6. Hao, Tang & Lei, Zhou & Tamio, Arai, 2008. "Optimization of a special case of continuous-time Markov decision processes with compact action set," European Journal of Operational Research, Elsevier, vol. 187(1), pages 113-119, May.
    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. Arruda, E.F. & Fragoso, M.D., 2015. "Solving average cost Markov decision processes by means of a two-phase time aggregation algorithm," European Journal of Operational Research, Elsevier, vol. 240(3), pages 697-705.

    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. Arruda, E.F. & Fragoso, M.D., 2015. "Solving average cost Markov decision processes by means of a two-phase time aggregation algorithm," European Journal of Operational Research, Elsevier, vol. 240(3), pages 697-705.
    2. Tugba Cayirli & Pinar Dursun & Evrim D. Gunes, 2019. "An integrated analysis of capacity allocation and patient scheduling in presence of seasonal walk-ins," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 524-561, June.
    3. Li, Yanjie & Yin, Baoqun & Xi, Hongsheng, 2011. "Finding optimal memoryless policies of POMDPs under the expected average reward criterion," European Journal of Operational Research, Elsevier, vol. 211(3), pages 556-567, June.
    4. Eduardo González & Leonardo Epstein & Verónica Godoy, 2012. "Optimal number of bypasses: minimizing cost of calls to wireless phones under Calling Party Pays," Annals of Operations Research, Springer, vol. 199(1), pages 179-191, October.
    5. Bruno Vieira & Derya Demirtas & Jeroen B. Kamer & Erwin W. Hans & Louis-Martin Rousseau & Nadia Lahrichi & Wim H. Harten, 2020. "Radiotherapy treatment scheduling considering time window preferences," Health Care Management Science, Springer, vol. 23(4), pages 520-534, December.
    6. Kaining Shao & Wenjuan Fan & Zishu Yang & Shanlin Yang & Panos M. Pardalos, 2022. "A column generation approach for patient scheduling with setup time and deteriorating treatment duration," Operational Research, Springer, vol. 22(3), pages 2555-2586, July.
    7. Tu-San Pham & Louis-Martin Rousseau & Patrick Causmaecker, 2022. "A two-phase approach for the Radiotherapy Scheduling Problem," Health Care Management Science, Springer, vol. 25(2), pages 191-207, June.
    8. I. Atencia & A. Pechinkin, 2013. "A discrete-time queueing system with optional LCFS discipline," Annals of Operations Research, Springer, vol. 202(1), pages 3-17, January.
    9. Silva, Thiago A.O. & de Souza, Mauricio C., 2020. "Surgical scheduling under uncertainty by approximate dynamic programming," Omega, Elsevier, vol. 95(C).
    10. Qu, Xiuli & Peng, Yidong & Shi, Jing & LaGanga, Linda, 2015. "An MDP model for walk-in patient admission management in primary care clinics," International Journal of Production Economics, Elsevier, vol. 168(C), pages 303-320.
    11. Gong, Jue & Liu, Shan, 2023. "Partially observable collaborative model for optimizing personalized treatment selection," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1409-1419.
    12. Ahmadi-Javid, Amir & Jalali, Zahra & Klassen, Kenneth J, 2017. "Outpatient appointment systems in healthcare: A review of optimization studies," European Journal of Operational Research, Elsevier, vol. 258(1), pages 3-34.
    13. Ulmer, Marlin W. & Thomas, Barrett W., 2020. "Meso-parametric value function approximation for dynamic customer acceptances in delivery routing," European Journal of Operational Research, Elsevier, vol. 285(1), pages 183-195.
    14. Marquinez, José Tomás & Sauré, Antoine & Cataldo, Alejandro & Ferrer, Juan-Carlos, 2021. "Identifying proactive ICU patient admission, transfer and diversion policies in a public-private hospital network," European Journal of Operational Research, Elsevier, vol. 295(1), pages 306-320.
    15. Agrawal, Deepak & Pang, Guodong & Kumara, Soundar, 2023. "Preference based scheduling in a healthcare provider network," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1318-1335.
    16. Rosita Guido & Giuseppe Ielpa & Domenico Conforti, 2020. "Scheduling outpatient day service operations for rheumatology diseases," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 102-128, March.
    17. Nazgol Niroumandrad & Nadia Lahrichi, 2018. "A stochastic tabu search algorithm to align physician schedule with patient flow," Health Care Management Science, Springer, vol. 21(2), pages 244-258, June.
    18. Liping Zhou & Na Geng & Zhibin Jiang & Shan Jiang, 2022. "Integrated Multiresource Capacity Planning and Multitype Patient Scheduling," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 129-149, January.
    19. Samorani, Michele & LaGanga, Linda R., 2015. "Outpatient appointment scheduling given individual day-dependent no-show predictions," European Journal of Operational Research, Elsevier, vol. 240(1), pages 245-257.
    20. Jaime González & Juan-Carlos Ferrer & Alejandro Cataldo & Luis Rojas, 2019. "A proactive transfer policy for critical patient flow management," Health Care Management Science, Springer, vol. 22(2), pages 287-303, June.

    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:ejores:v:229:y:2013:i:1:p:190-198. 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/locate/eor .

    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.