IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v39y1991i6p970-978.html
   My bibliography  Save this article

A State Aggregation Approach to Manufacturing Systems Having Machine States with Weak and Strong Interactions

Author

Listed:
  • J. Jiang

    (University of Toronto, Toronto, Canada)

  • S. P. Sethi

    (University of Toronto, Toronto, Canada)

Abstract

A hierarchical approach to control a manufacturing system, subject to multiple machine states modeled by a Markov process with weak and strong interactions, is suggested. The idea is to aggregate strongly interacting or high transition probability states within a group of states and consider only the transition between these groups for the analysis of the system in the long run. We show that such an aggregation results in a problem of reduced size, whose solution can be modified in a simple way to obtain an asymptotically optimal feedback solution to the original problem. Also, an example is solved to illustrate the results developed in the paper.

Suggested Citation

  • J. Jiang & S. P. Sethi, 1991. "A State Aggregation Approach to Manufacturing Systems Having Machine States with Weak and Strong Interactions," Operations Research, INFORMS, vol. 39(6), pages 970-978, December.
    • Handle: RePEc:inm:oropre:v:39:y:1991:i:6:p:970-978
    DOI: 10.1287/opre.39.6.970
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.39.6.970
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.39.6.970?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. 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.
    2. S. P. Sethi & Q. Zhang, 1998. "Near Optimization of Dynamic Systems by Decomposition and Aggregation," Journal of Optimization Theory and Applications, Springer, vol. 99(1), pages 1-22, October.
    3. Parpas, Panos & Webster, Mort, 2014. "A stochastic multiscale model for electricity generation capacity expansion," European Journal of Operational Research, Elsevier, vol. 232(2), pages 359-374.
    4. Q. Zhang & G. Yin & E. K. Boukas, 1997. "Controlled Markov Chains with Weak and Strong Interactions: Asymptotic Optimality and Applications to Manufacturing," Journal of Optimization Theory and Applications, Springer, vol. 94(1), pages 169-194, July.

    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:oropre:v:39:y:1991:i:6:p:970-978. 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.