A Discriminative Multi-Objective Programming Method for Solving Network DEA
This study proposes the multi-objective programming (MOP) method for solving network DEA models. In the proposed method, the efficiency of each division (within a DMU) and the overall efficiency of the DMU are formulated as different objective functions. By the fuzzy approach, the overall as well as the divisional efficiencies can be computed in a cohesive framework. We adopt information entropy to estimate the discriminating power of various DEA models. The results show that this study obtains satisfactory and discriminating efficiency scores compared with related studies.
KeywordsNetwork DEA BCC models multi-objective programming information entropy
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