Improvement of discrimination power and weight dispersion in multi-criteria data envelopment analysis

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Date
2014-09
Authors
Ghasemi, Mohammadreza
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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
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Discrimination Power
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