Halme, Joro, Korhonen, Salo and Wallenius [1998] present an approach for using preference information in Data Envelopment Analysis (DEA). In their paper, they develop value efficiency, an extension of the concept of efficiency in DEA. In this paper, we develop some related concepts, and present two refinements: 1. Some bounds on the value efficiency scores that may be obtained without any decis...

A characteristic of data envelopment analysis (DEA) is to allow individual decision making units (DMUs) to select the factor weights that are the most advantages 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 same s...

Data envelopment analysis (DEA) is proposed in this paper to generate local weights of alternatives from pair-wise comparison judgment matrices used in the analytic hierarchy process (AHP). The underlying assumption behind the approach is explained, and some salient features are explored. It is proved that DEA correctly estimates the true weights when applied to a consistent matrix formed using...

Data envelopment analysis (DEA) is a widely used mathematical programming approach for comparing the input and output of a set of comparable decision-making units (DMUs) by evaluating their relative efficiency. The traditional DEA methods require accurate measurement of both the inputs and outputs. However, the real evaluation of the DMUs is often characterized by imprecision and uncertainty in...

In the present time, evaluating the performance of banks is one of the important subjects for societies and the bank managers who want to expand the scope of their operation. One of the non-parametric approaches for evaluating efficiency is data envelopment analysis(DEA). By a mathematical programming model, DEA provides an estimation of efficiency surfaces. A major problem faced by DEA is that...

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...

In this paper, we discuss the relationship between multi-attribute utility theory and DEA models without explicit inputs (DEA-WEI), including dual models and some theoretical analysis of DEA-WEI models. We then propose generic DEA-WEI models with quadratic utility terms. Finally, we provide illustrative examples to show that DEA-WEI with suitable quadratic utility terms are able to reflect some...

Keywords: Data envelopment analysis Target unit Undesirable output Multiple objective linear programming Minimax method a b s t r a c t Data Envelopment Analysis (DEA) is a mathematical programming technique for identifying efficient Decision Making Units (DMUs) with multiple inputs and multiple outputs. DEA provides a technical efficiency score for each DMU, a technical efficiency reference se...

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...

Evaluation of efficiency of each of the DMUs (Decision Making Units) in a company is a very important task. Thus, the studies of evaluation of efficiency are being actively carried out, based on production function. Until quite recently, the loglinear production function (the Cobb-Douglas function) has been used for evaluation purposes. The loglinear model evaluates the DMUs by measuring the av...