IDEAS home Printed from
   My bibliography  Save this article

Enhanced Russell measure in fuzzy DEA


  • Meiqiang Wang
  • Yongjun Li


The radial measures of classical DEA models (CCR, BCC) are incomplete, they are only separate measures of input and output efficiency and their efficiency index omit the non-zero input and output slacks. Enhanced Russell graph measure (ERM) eliminates these deficiencies. All of the existing fuzzy DEA models are extension of CCR or BCC model, efficiencies of DMUs, ultimately, are solution of CCR or BCC model. Based on ERM model, a fuzzy DEA model is proposed to deal with the efficiency evaluation problem with the given fuzzy input and output data, by using a ranking method based on the comparison of α-cuts. The proposed framework is illustrated through an application to performance assessment of flexible manufacturing system and comparative results are presented. The efficiency measure of the proposed approach is relatively more reasonable than those of fuzzy DEA models based on CCR or BCC model and represents some real-life processes more appropriately.

Suggested Citation

  • Meiqiang Wang & Yongjun Li, 2010. "Enhanced Russell measure in fuzzy DEA," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 2(2), pages 140-154.
  • Handle: RePEc:ids:injdan:v:2:y:2010:i:2:p:140-154

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.


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

    Cited by:

    1. K. Smimou, 2013. "On the significance testing of fuzzy regression applied to the CAPM: Canadian commodity futures evidence," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 5(2), pages 144-171.


    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:ids:injdan:v:2:y:2010:i:2:p:140-154. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Darren Simpson). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.