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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2010, 15(1), 148-155; https://doi.org/10.3390/mca15010148

Ranking Decision Making Units with Stochastic Data by Using Coefficient of Variation

1
Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
2
Department of Statistics, Faculty of Economics, Allameh Tabataba'i University, Tehran, Iran
3
Ph.D Student, Department of Statistics, Science and Research Branch, Islamic Azad University, 1477893855, Tehran, Iran
*
Author to whom correspondence should be addressed.
Published: 1 April 2010
PDF [145 KB, uploaded 1 April 2016]

Abstract

Data Envelopment Analysis (DEA) is a non-parametric technique which is based on mathematical programming for evaluating the efficiency of a set of Decision Making Units (DMUs). Throughout applications, managers encounter with stochastic data and the necessity of having a method that is able to evaluate efficiency and rank efficient units has been under consideration. In this paper considering the concept of coefficient of variation among efficient DMUs, two ranking methods has been proposed. Within these ranking methods, a DMU will have a higher rank if it's coefficient of variation be smaller. These methods are suitable when managers are able to determine weights on coefficient of variations or on inputs and outputs. At the end we applied these methods on a numerical example.
Keywords: Coefficient of variation; Data envelopment analysis; Ranking Coefficient of variation; Data envelopment analysis; Ranking
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Lotfi, F.H.; Nematollahi, N.; Behzadi, M.; Mirbolouki, M. Ranking Decision Making Units with Stochastic Data by Using Coefficient of Variation. Math. Comput. Appl. 2010, 15, 148-155.

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