Abstract: In DEA, there are typically two schemes for measuring efficiency of DMUs; radial and non-radial. Radial models assume proportional change of inputs/outputs and usually remaining slacks are not directly accounted for inefficiency. On the other hand, non-radial models deal with slacks of each input/output individually and independently, and integrate them into an efficiency measure, cal...

One of the weak points of DEA (Data Envelopment Analysis) models indicated in literature [1,2] is their sensitivity to variable measurement errors. The occurrence of data interference, which is the basis of the productivity analysis, may distort the classification of the units and may cause misjudgement of their effectiveness. In the article the results of simulation concerning the DEA models s...

This paper proposes two-stage model for prioritizating decision making units (DMU) where each unit has multiple inputs and outputs. The rst stage model is a noncooperative game where each optimal input-output weight set of DMU is de ned by the Data Envelopment Analysis (DEA) and each DMU evaluates other DMUs by its own weights. The equilibrium state obtained from the rst stage motivates none of...

This paper discusses and reviews the use of super-eciency approach in data envelopment analysis (DEA) sensitivity analyses. It is shown that super-eciency score can be decomposed into two data perturbation components of a particular test frontier decision making unit (DMU) and the remaining DMUs. As a result, DEA sensitivity analysis can be done in (1) a general situation where data for a tes...

It is well established that multiple reference sets may occur for a decision making unit (DMU) in the non-radial DEA (data envelopment analysis) setting. As our first contribution, we differentiate between three types of reference set. First, we introduce the notion of unary reference set (URS) corresponding to a given projection of an evaluated DMU. The URS includes efficient DMUs that are act...

DEA (Deta Envelopment Analysis) is a non-parametric technique for measuring the efficiency of DMUs (Decision Making Units)with common inputs and outputs [2,5]. viewpoint for each DMU because of taking a maximum ratio. During recent years, the issue of sensitivity and stability of data envelopment analysis results has been extensively studied. The first DEA sensitivity analysis paper by Charnes ...

Cross-efficiency evaluation is an effective way of ranking decision making units (DMUs) in data envelopment analysis (DEA). Existing approaches for cross-efficiency evaluation are mainly focused on the calculation of cross-efficiency matrix, but pay little attention to the aggregation of the efficiencies in the cross-efficiency matrix. The most widely used approach is to aggregate the efficienc...

Data envelopment analysis is a new field of interdisciplinary research in operations management science and mathematical economics. uses programming to evaluate the relationship between decision-making units with multiple inputs outputs (abbreviated as DMU). Effectiveness (DEA effective) judge whether DMU located on "frontier surface" production possibility set. This paper proposes method for e...

sohrabi and nalchigar (2010) proposed a new data envelopment analysis (dea) model to identify the most efficient decision-making unit (dmu) in presence of imprecise data. in this paper, it is shown that the proposed model is not able to determine the most efficient dmu and is randomly introduced an efficient dmu. in addition, it is shown that this model determines the most efficient dmu in the ...

Data envelopment analysis (DEA) calculates the relative efficiency of homogenous decision-making units (DMUs) with multiple inputs and outputs. Classic DEA models usually suffer from several issues such as: discrimination power, variable weights of inputs/outputs, inaccurate efficiency estimation for small number of DMUs, incapability in working with zero and negative data, and not having exter...