IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v78y1998i0p219-23410.1023-a1018902200919.html
   My bibliography  Save this article

Decision support system for scheduling a Flexible Flow System: Incorporation of feature construction

Author

Listed:
  • Selwyn Piramuthu
  • Narayan Raman
  • Michael Shaw

Abstract

We study the problem of scheduling a Flexible Flow System (FFS) while utilizing machine learning techniques. Specifically, the effect of incorporating Feature Construction in an existing adaptive learning-based decision support system is investigated. The primary purpose of constructing features is to increase the efficiency of obtaining information from available data by using new features in addition to the initial set of features. There is a need to obtain information effectively while scheduling an FFS. The incorporation of feature construction in such a framework is thus beneficial, which is documented in our preliminary results. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Selwyn Piramuthu & Narayan Raman & Michael Shaw, 1998. "Decision support system for scheduling a Flexible Flow System: Incorporation of feature construction," Annals of Operations Research, Springer, vol. 78(0), pages 219-234, January.
    • Handle: RePEc:spr:annopr:v:78:y:1998:i:0:p:219-234:10.1023/a:1018902200919
    DOI: 10.1023/A:1018902200919
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1018902200919
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1018902200919?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.

    Citations

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


    Cited by:

    1. Weiwei Chen & Jie Song & Leyuan Shi & Liang Pi & Peter Sun, 2013. "Data mining-based dispatching system for solving the local pickup and delivery problem," Annals of Operations Research, Springer, vol. 203(1), pages 351-370, March.
    2. Wei Zhou & Selwyn Piramuthu, 2017. "Identification shrinkage in inventory management: an RFID-based solution," Annals of Operations Research, Springer, vol. 258(2), pages 285-300, November.
    3. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.

    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:spr:annopr:v:78:y:1998:i:0:p:219-234:10.1023/a:1018902200919. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.