Ranking of different common set of weights models using a voting model and its application in determining efficient DMUs
by Mehdi Soltanifar
International Journal of Advanced Operations Management (IJAOM), Vol. 3, No. 3/4, 2011

Abstract: In most models of data envelopment analysis (DEA), the best performers have the efficiency score of unity. Usually there are plural decision making units (DMUs) which have this 'efficient status' and DEA cannot provide more information about these efficient DMUs. Discrimination between these efficient DMUs is an interesting research subject. In effect, most conventional methods to rank efficient DMUs are based on the concept of common weights analysis (CWA). In this paper, after reviewing the existing common weights models, we propose a new methodology to rank the common weights models for the performance indices of only DEA efficient DMUs based on a voting model. Also, an approach for combining the results obtained from the common weights models is presented. Then, we give an example to illustrate our approach, and finally the new method is employed to rank efficient units in a real world problem.

Online publication date: Tue, 14-Feb-2012

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Operations Management (IJAOM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com