data envelopment analysis (dea) is a method used for measuring the efficiency of decision-making units‎. unlike the standard models‎, ‎which assume decision-making units to be a black box‎, ‎network data envelopment analysis focuses on the internal structure of these units‎. ‎some researchers have developed a two-stage method where all the inputs are entirely used in the first stage‎, ‎producin...

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

this paper introduces discretionary imprecise data in data envelopment analysis (dea) and discusses the efficiency evaluation of decision making units (dmus) with non-discretionary imprecise data. then, suggests a method for evaluation the efficiency of dmus with non-discretionary imprecise data. when some inputs and outputs are imprecise and non-discretionary, the dea model becomes non-linear ...

Data Envelopment Analysis (DEA) is the most commonly used approach for evaluating healthcare efficiency (Hollingsworth, 2008), but a long-standing concern is that DEA assumes that data are measured without error. This is quite unlikely, and DEA and other efficiency analysis techniques may yield biased efficiency estimates if it is not realized (Gajewski, Lee, Bott, Piamjariyakul and Taunton, 20...

The Trade Offs approach is an advanced tool for the improvement of the discrimination of Data Envelopment Analysis (DEA) models, Meta Malmquist Index was defined by Maria Portella and et. al (2008). In this paper we compute the Meta Malmquist Index in Trade Offs model in DEA and we compare, obtaining results, of Meta Malmquist Index in different models of DEA, Variable Return to Scale (VRS), Co...

Data envelopment analysis (DEA) mainly utilizes envelopment technology to replace production function in microeconomics. The input and output of decision making units (DMUs) are projected into the attributes to evaluate or measure their performance. However, if the inputs and outputs are linguistically termed or are fuzzy-numbered, conventional DEA can not easily measure the performance. Theref...

This paper presents a simplified version of Data Envelopment Analysis (DEA) a conventional approach to evaluating the performance and ranking of competitive objects characterized by two groups of factors acting in opposite directions: inputs and outputs. DEA with a Perfect Object (DEA PO) augments the group of actual objects with a virtual Perfect Object the one having greatest outputs and smal...

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...

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers ...