IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v14y1968i5p292-300.html
   My bibliography  Save this article

Turnpike Planning Horizons for a Markovian Decision Model

Author

Listed:
  • Jeremy F. Shapiro

    (Massachusetts Institute of Technology)

Abstract

This paper establishes some asymptotic properties of the finite state and action space Markovian decision model. For the discounted case, a turnpike theorem is proven which states that an optimal immediate decision when the planning horizon is sufficiently large is to choose one of the decisions which is optimal when the planning horizon is infinite. An upper bound on how large is sufficiently large is given. An asymptotic property of the model for discount factors in a neighborhood of one is also developed.

Suggested Citation

  • Jeremy F. Shapiro, 1968. "Turnpike Planning Horizons for a Markovian Decision Model," Management Science, INFORMS, vol. 14(5), pages 292-300, January.
    • Handle: RePEc:inm:ormnsc:v:14:y:1968:i:5:p:292-300
    DOI: 10.1287/mnsc.14.5.292
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.14.5.292
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.14.5.292?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nuthall, Peter L., 1980. "A Survey of Methods for Determining A Planning Horizon," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 48(01), pages 1-15, April.
    2. Vassili Kolokoltsov & Wei Yang, 2012. "Turnpike Theorems for Markov Games," Dynamic Games and Applications, Springer, vol. 2(3), pages 294-312, September.
    3. Mark E. Lewis & Anand Paul, 2019. "Uniform Turnpike Theorems for Finite Markov Decision Processes," Management Science, INFORMS, vol. 44(4), pages 1145-1160, November.
    4. Suresh Chand & Vernon Ning Hsu & Suresh Sethi, 2002. "Forecast, Solution, and Rolling Horizons in Operations Management Problems: A Classified Bibliography," Manufacturing & Service Operations Management, INFORMS, vol. 4(1), pages 25-43, September.
    5. M. Jacobson & N. Shimkin & A. Shwartz, 2003. "Markov Decision Processes with Slow Scale Periodic Decisions," Mathematics of Operations Research, INFORMS, vol. 28(4), pages 777-800, November.

    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:inm:ormnsc:v:14:y:1968:i:5:p:292-300. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.