A New Method for Improving the Discrimination Power and Weights Dispersion in the Data Envelopment Analysis

S. Kordrostami, A. Mirmousavi


The appropriate choice of input-output weights is necessary
to have a successful DEA model. Generally, if the number of
DMUs i.e., n, is less than number of inputs and outputs i.e., m+s,
then many of DMUs are introduced as efficient then the discrimination
between DMUs is not possible. Besides, DEA models are free to
choose the best weights. For resolving the problems that are resulted
from freedom of weights, some constraints are set on the input-output
weights. Symmetric weight constraints are a kind of weight constrains.
In this paper, we represent a new model based on a multi-criterion
data envelopment analysis (MCDEA) are developed to moderate the
homogeneity of weights distribution by using symmetric weight constrains.
Consequently, we show that the improvement of the dispersal
of unrealistic input-output weights and the increasing discrimination
power for our suggested models. Finally, as an application of the new
model, we use this model to evaluate and ranking guilan selected hospitals.


Data envelopment analysis, discrimination power, weights dispersion, symmetric weight constraints, multiple optimal weights, ranking

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