IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v178y2007i3p808-818.html
   My bibliography  Save this article

A policy gradient method for semi-Markov decision processes with application to call admission control

Author

Listed:
  • Singh, Sumeetpal S.
  • Tadic, Vladislav B.
  • Doucet, Arnaud

Abstract

No abstract is available for this item.

Suggested Citation

  • Singh, Sumeetpal S. & Tadic, Vladislav B. & Doucet, Arnaud, 2007. "A policy gradient method for semi-Markov decision processes with application to call admission control," European Journal of Operational Research, Elsevier, vol. 178(3), pages 808-818, May.
  • Handle: RePEc:eee:ejores:v:178:y:2007:i:3:p:808-818
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(06)00131-7
    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. Daniel Adelman, 2003. "Price-Directed Replenishment of Subsets: Methodology and Its Application to Inventory Routing," Manufacturing & Service Operations Management, INFORMS, vol. 5(4), pages 348-371, May.
    2. Gosavi, Abhijit, 2004. "Reinforcement learning for long-run average cost," European Journal of Operational Research, Elsevier, vol. 155(3), pages 654-674, June.
    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. Abhijit Gosavi, 2009. "Reinforcement Learning: A Tutorial Survey and Recent Advances," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 178-192, May.
    2. Huang, Yonghui & Guo, Xianping, 2011. "Finite horizon semi-Markov decision processes with application to maintenance systems," European Journal of Operational Research, Elsevier, vol. 212(1), pages 131-140, July.
    3. Zhang, Zhicong & Zheng, Li & Hou, Forest & Li, Na, 2011. "Semiconductor final test scheduling with Sarsa([lambda], k) algorithm," European Journal of Operational Research, Elsevier, vol. 215(2), pages 446-458, December.
    4. Hao-Xiang Wang & Hong-Sen Yan, 2016. "An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1085-1095, October.
    5. Yonghui Huang & Xianping Guo & Xinyuan Song, 2011. "Performance Analysis for Controlled Semi-Markov Systems with Application to Maintenance," Journal of Optimization Theory and Applications, Springer, vol. 150(2), pages 395-415, August.
    6. Schütz, Hans-Jörg & Kolisch, Rainer, 2012. "Approximate dynamic programming for capacity allocation in the service industry," European Journal of Operational Research, Elsevier, vol. 218(1), pages 239-250.
    7. Li, Yanjie & Cao, Fang, 2013. "A basic formula for performance gradient estimation of semi-Markov decision processes," European Journal of Operational Research, Elsevier, vol. 224(2), pages 333-339.

    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. Duraikannan Sundaramoorthi & Victoria Chen & Jay Rosenberger & Seoung Kim & Deborah Buckley-Behan, 2010. "A data-integrated simulation-based optimization for assigning nurses to patient admissions," Health Care Management Science, Springer, vol. 13(3), pages 210-221, September.
    2. Cárdenas-Barrón, Leopoldo Eduardo & González-Velarde, José Luis & Treviño-Garza, Gerardo & Garza-Nuñez, Dagoberto, 2019. "Heuristic algorithm based on reduce and optimize approach for a selective and periodic inventory routing problem in a waste vegetable oil collection environment," International Journal of Production Economics, Elsevier, vol. 211(C), pages 44-59.
    3. Li, Xueping & Wang, Jiao & Sawhney, Rapinder, 2012. "Reinforcement learning for joint pricing, lead-time and scheduling decisions in make-to-order systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 99-109.
    4. Soumia Ichoua & Michel Gendreau & Jean-Yves Potvin, 2006. "Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching," Transportation Science, INFORMS, vol. 40(2), pages 211-225, May.
    5. Barlow, E. & Bedford, T. & Revie, M. & Tan, J. & Walls, L., 2021. "A performance-centred approach to optimising maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 292(2), pages 579-595.
    6. Daniel Adelman & Diego Klabjan, 2005. "Duality and Existence of Optimal Policies in Generalized Joint Replenishment," Mathematics of Operations Research, INFORMS, vol. 30(1), pages 28-50, February.
    7. Safaei, Fatemeh & Ahmadi, Jafar & Taghipour, Sharareh, 2022. "A maintenance policy for a k-out-of-n system under enhancing the system’s operating time and safety constraints, and selling the second-hand components," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    8. Stephane R. A. Barde & Soumaya Yacout & Hayong Shin, 2019. "Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 147-161, January.
    9. Alejandro Toriello & George Nemhauser & Martin Savelsbergh, 2010. "Decomposing inventory routing problems with approximate value functions," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(8), pages 718-727, December.
    10. Nicola Secomandi & François Margot, 2009. "Reoptimization Approaches for the Vehicle-Routing Problem with Stochastic Demands," Operations Research, INFORMS, vol. 57(1), pages 214-230, February.
    11. Daniel Adelman, 2007. "Price-Directed Control of a Closed Logistics Queueing Network," Operations Research, INFORMS, vol. 55(6), pages 1022-1038, December.
    12. Yang, Hongbing & Li, Wenchao & Wang, Bin, 2021. "Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    13. Daniel Adelman & Diego Klabjan, 2012. "Computing Near-Optimal Policies in Generalized Joint Replenishment," INFORMS Journal on Computing, INFORMS, vol. 24(1), pages 148-164, February.
    14. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    15. Qihang Lin & Selvaprabu Nadarajah & Negar Soheili, 2020. "Revisiting Approximate Linear Programming: Constraint-Violation Learning with Applications to Inventory Control and Energy Storage," Management Science, INFORMS, vol. 66(4), pages 1544-1562, April.
    16. Jin-Hwa Song & Martin Savelsbergh, 2007. "Performance Measurement for Inventory Routing," Transportation Science, INFORMS, vol. 41(1), pages 44-54, February.
    17. Jason Acimovic & Stephen C. Graves, 2015. "Making Better Fulfillment Decisions on the Fly in an Online Retail Environment," Manufacturing & Service Operations Management, INFORMS, vol. 17(1), pages 34-51, February.
    18. Schütz, Hans-Jörg & Kolisch, Rainer, 2012. "Approximate dynamic programming for capacity allocation in the service industry," European Journal of Operational Research, Elsevier, vol. 218(1), pages 239-250.
    19. Alejandro Toriello & William B. Haskell & Michael Poremba, 2014. "A Dynamic Traveling Salesman Problem with Stochastic Arc Costs," Operations Research, INFORMS, vol. 62(5), pages 1107-1125, October.
    20. Jian Wang & Murtaza Das & Stephen Tappert, 2021. "Applying reinforcement learning to estimating apartment reference rents," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 330-343, June.

    More about this item

    Statistics

    Access and download statistics

    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:178:y:2007:i:3:p:808-818. 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.