Data Envelopment Analysis appeared in 1978 when the first model, known as CCR was proposed by Charnes et al. (1978). This model calculates the efficiency of productive units, known as DMUs Decision Making Units, by comparing the use of resources (inputs) and the production (outputs) obtained. This model considers Constant Returns to Scale (CRS), i.e., an increase in resources generates a propor...

the malmquist productivity index evaluates the productivity change of a decision making unit (dmu) between two time periods. in this current study, a method is proposed to compute the malmquist productivity index in several time periods (from the first to the last periods) in data envelopment analysis (dea) and then, the obtained malmquist productivity index is compared with malmquist productiv...

The purpose of this study is to utilize a new method for ranking extreme efficient decision making units (DMUs) based upon the omission of these efficient DMUs from reference set of inefficient and non-extreme efficient DMUs in data envelopment analysis (DEA) models with constant and variable returns to scale. In this method, an L2norm is used and it is believed that it doesn't have any existin...

In this current study a generalized super-efficiency model is first proposed for ranking extreme efficient decision making units (DMUs) in stochastic data envelopment analysis (DEA) and then, a deterministic (crisp) equivalent form of the stochastic generalized super-efficiency model is presented. It is shown that this deterministic model can be converted to a quadratic programming model. So fa...

Data envelopment analysis (DEA) defines relative efficiency of decision making units (DMUs), using mathmatical programming. In evaluating relative efficiency, usually more than one unit may be efficient. The problem of ranking efficient DMUs is of interest from theoretical and practical point of view. In this paper different methods have been discussed and in some sense they are compared. Real ...

In models of data envelopment analysis (DEA), an optimal set of weights is generally assumed to represent the assessed decision making unit (DMU) in the best light in comparison to all the other DMUs, and so there is an optimal set of weights corresponding to each DMU. The present paper, proposes a three stage method to determine one common set of weights for decision making units. Then, we use...

Data envelopment analysis (DEA) is a non-parametric approach for measuring the efficiency of decision making units (DMUs) that use multiple inputs in order to produce multiple outputs. In most real applications, DMUs have a two-stage network process which can be used for management of organizations such as hospitals, insurance companies, banks, and etc. The data are crisp in the standard DEA mo...

Data envelopment analysis (DEA) is a mathematical programming method in Operations Research that can be used to distinguish between efficient and inefficient decision making units (DMUs). However, the conventional DEA models do not have the ability to rank the efficient DMUs. This article suggests bootstrapping method for ranking measures of technical efficiency as calculated via non-radial mod...

In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-laye...

One of the difficulties of Data Envelopment Analysis(DEA) is the problem of deciency discriminationamong efficient Decision Making Units(DMUs) and hence, yielding large number of DMUs as efficientones. The main purpose of this paper is to overcome this inability. One of the methods for rankingefficient DMUs is minimizing the Coefficient of Variation (CV) for inputs-outputs weights. In this pape...