The existing data envelopment analysis (DEA) models for measuring the relative efficiencies of a set of decision making units (DMUs) using various inputs to produce various outputs are limited to crisp data. The notion of fuzziness has been introduced to deal with imprecise data. Fuzzy DEA models are made more powerful for applications. This paper develops the measure of efficiencies in input o...

data envelope analysis (dea) is an approach to estimate the relative efficiency of decision making units (dmus). several studies were conducted in order to prioritize efficient units and some useful models such as cross-efficiency matrix (cem) were presented.  besides, a number of dea models with interval data have been developed and ranking dmus with such data was solved. however, presenting a...

a characteristic of data envelopment analysis (dea) is to allow individual decision making units (dmus) to select the factor weights which are the most advantageous for them in calculating their efficiency scores. this flexibility in selecting the weights, on the other hand, deters the comparison among dmus on a common base. for dealing with this difficulty and assessing all the dmus on the sam...

Rough data envelopment analysis (RDEA) evaluates the performance of the decision making units (DMUs) under rough uncertainty assumption. In this paper, new discussion regarding RDEA is extended. The RSBM model is proposed by integrating SBM model and rough set theory. The process of reaching solution is presented and this model is applied to efficiency evaluation of the DMUs with uncertain ...

This article compares two techniques: Data Envelopment Analysis (DEA) and Factor Analysis (FA) to aggregate multiple inputs and outputs in the evaluation of decision making units (DMU). Dِata envelopment analysis (DEA), a popular linear programming technique, is useful to rate comparatively operational efficiency of DMUs based on their deterministic or stochastic input–output data. Factor analys...

As a useful management and decision tool, data envelopment analysis (DEA) has become a pop area of research. One of the topics of interests in DEA is sensitivity and stability analysis of decision making units (DMUs). Due to the uncertainty of the data in real life, this paper will give some DEA models in fuzzy environment. It is followed by a series analysis of sensitivity and stability for al...

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...

This paper introduces two virtual decision making units (DMUs) called ideal DMU (IDMU) and anti-ideal DMU (ADMU) into the data envelopment analysis (DEA). The resultant DEA models are, respectively, referred to as the data envelopment analysis with ideal and anti-ideal decision making units. One evaluates DMUs from the viewpoint of the best possible relative efficiency, while the other evaluate...

data envelopment analysis (dea) is a mathematical programming-based approach for evaluates the relative efficiency of a set of dmus (decision making units). the relative efficiency of a dmu is the result of comparing the inputs and outputs of the dmu and those of other dmus in the pps (production possibility set). also, in data envelopment analysis various models have been developed in order to...

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...