Data envelopment analysis (DEA) identifies an empirical efficient frontier of a set of peer decision making units (DMUs) with multiple inputs and outputs. The efficient frontier is characterized by the DMUs with an unity efficiency score. The performance of inefficient DMUs is characterized with respect to the identified efficient frontier. If the performance of inefficient DMUs deteriorates or...

This research proposes a new ranking system for extreme efficient DMUs (Decision Making Units) based upon the omission of these efficient DMUs from reference set of the inefficient DMUs. We state and prove some facts related to our model. A numerical example where the proposed method is compared with traditional ranking approaches is shown. 2006 Elsevier B.V. All rights reserved.

This paper adopts a modified approach of data envelopment analysis (DEA) to measure the academic efficiency of university departments. In real-world case studies, conventional DEA models often identify too many decision-making units (DMUs) as efficient. This occurs when the number of DMUs under evaluation is not large enough compared to the total number of decision variables. To overcome this l...

Envelopment Analysis (DEA) is a very eective method to evaluate the relative eciency of decision-making units (DMUs). DEA models divided all DMUs in two categories: ecient and inecientDMUs, and don't able to discriminant between ecient DMUs. On the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ordinal da...

This paper discusses and reviews the use of super-eciency approach in data envelopment analysis (DEA) sensitivity analyses. It is shown that super-eciency score can be decomposed into two data perturbation components of a particular test frontier decision making unit (DMU) and the remaining DMUs. As a result, DEA sensitivity analysis can be done in (1) a general situation where data for a tes...

In this paper, we reformulate the conventional DEA models as an imprecise problem and propose a novel method for evaluating DMUs when inputs outputs are fuzzy and/or ordinal or vary in intervals. For purpose, convert all data into interval data. order to each number data, use nearest weighted approximation of numbers by applying weighting function, one. manner, could The presented determine eff...

Sufficient conditions for simultaneous efficiency preservation of all efficient Decision Making Units (DMUs) and for inefficiency preservation of all inefficient DMUs in the Additive model of Data Envelopment Analysis (DEA) under the simultaneous non-negative perturbations of all data of all DMUs are obtained. An illustrative example is provided.

In data envelopment analysis (DEA) efficient decision making units (DMUs) are of primary importance as they define the efficient frontier. By means of modified CCR model, in which the test DMU is excluded from the reference set, we are able to determine what perturbations of data can be tolerated before frontier DMUs become nonfrontier. In this paper we discuss simultaneous data perturbations i...

It is a general precondition of Data Envelopment Analysis (DEA) that all Decision Making Units (DMUs) to be evaluated are homogeneous. However, it is hard to find a method in the literature to judge whether DMUs are homogeneous or not. This paper firstly analyzes the impacts of DMUs’ non-homogeneity caused by technology difference on the efficiency estimates. It is showed that the DMU under eva...

The conventional Data Envelopment Analysis (DEA) model considers Decision Making Units (DMUs) as a black box, meaning that these models do not consider the connection and the inner structures of DMUs. Moreover, these models consider that the activities of DMUs in each time are independent of other times, but in the real world, the inner structures of DMUs are complicated, and the activities of ...