this research identifies returns to scale (rts) of efficient decision making units (dmus) with desirable (good) and undesirable (bad) inputs and outputs by presenting a new dea (data envelopment analysis) approach. in this study, we first introduce a new input-output oriented model to determine efficient dmus in the presence of undesirable factors and then, returns to scale of these dmus are es...

bankruptcy in the same amount of time and history is very rampant and therefore the vision of the future can be prevented. using data envelopment analysis (dea) and malmquist index can precise evaluating of the performances of many different kinds of decision making units (dmu) such as hospitals, universities, business firms, etc. in this paper, we will modify directional distance formulation o...

For better guiding a system, senior managers should have accurate information. Using Data Envelopment analysis (DEA) help managers in this objective. Thus, many investigations have been made in order to find the most productive scale size (MPSS) for the evaluating decision making units (DMUs). In this paper we consider this case where there exist subsets of input and output variables to be inte...

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...

cross efficiency is a method with the idea of peer evaluation instead of self-evaluation, and is used for evaluation and ranking decision making units (dmus) in data envelopment analysis (dea). unlike most existing dea ranking models which can only rank a subset of dmus, for example non-efficient or extreme efficient dmus, cross efficiency can rank all dmus, even non-extreme ones. however, sinc...

In original data envelopment analysis (DEA) models, the data for all inputs and outputs are known exactly. When some inputs and outputs are unknown decision variables, such as interval data, ordinal data, and ratio bounded data, the DEA model is called imprecise DEA (IDEA). In this paper, We develop an alternative approach based upon slacksbased measure of efficiency (SBM) for dealing with inte...

The aim of this paper is to evaluate the performance of the faculties of Islamic Azad University of Zahedan Branch based on two-component (teaching and research) decision making units (DMUs) in data envelopment analysis (DEA). Nowadays it is obvious that most of the systems as DMUs do not act as a simple inputoutput structure. Instead, if they have been studied more delicately, they include net...

‎Data envelopment analysis (DEA) is an objective method for priority determination of decision making units (DMUs) with the same multiple inputs and outputs. DEA is an efficiency estimation technique, but it can be used for solving many problems of management such as rankig of DMUs. Many researchers have found similarity between DEA and MCDM techniques. One of the earliest techniques in MCDM is...

Data Envelopment Analysis (DEA) was developed to help compare the relative performance of decision-making units. It is a non-parametric method for performing frontier analysis. It uses linear programming to estimate the efficiency of multiple decision-making units and it is commonly used in production, management and economics [3]. DEA generates an efficiency score between 0 and 1 for each unit...