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Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer decision making units (DMUs) with multiple input and outputs. Beside of its popularity, DEA has some drawbacks such as unrealistic input–output weights and lack of discrimination among efficient DMUs. In this study, two new models based(More)
In this paper, a new classification model which combines the discriminant analysis and the data envelopment analysis and bases on on the multicriteria decision making is developed. Our suggested model utilizes the relative efficiency concept of the data envelopment analysis in predicting group membership of units. The study is supported with an application(More)
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