Improvement of discrimination power and weight dispersion in multi-criteria data envelopment analysis
Loading...
Date
2014-09
Authors
Ghasemi, Mohammadreza
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Lack of discrimination power and poor weight dispersion remain major issues in
Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA
(MCDEA) model developed in the late 1990s, only goal programming approaches;
that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said
problems in a multi-objective framework. This study finds GPDEA models to be
invalid and demonstrates that the proposed bi-objective multiple criteria DEA (BiOMCDEA)
outperforms the GPDEA models in the aspects of discrimination power
and weight dispersion, as well as requiring less computational codes. In addition, this
study proposed an extension model named as BiO-WeR that provides additional
weight restrictions (WeR) in order to distribute the values of input-output weights
more evenly than those obtained by the initial BiO-MCDEA model. Lastly, the
concept of fuzzy set theory is used to account for the uncertainty in the
corresponding output weights. This study then implements the BiO-WeR model with
fuzzy restrictions on the output weights as a means to further reduce the number of
efficient DMUs and improve the discrimination power. An application of energy
dependency among 25 European Union member countries is further used to describe
the efficacy and demonstrate the implementation of the proposed approaches
Description
Keywords
Discrimination Power