An Integrated Data Envelopment Analysis and Simulation Method for Group Consensus Ranking
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Group consensus ranking is an important topic in performance evaluation and selection research. Data envelopment analysis (DEA) has been used for obtaining an efficiency score (preference score) for each candidate. We propose an integrated DEA and simulation method for group consensus ranking. The ranking method proposed in this study has several unique features. In contrast to most voting methods that assume equal voting power to voters, the proposed method classifies voters into different groups and allows for assigning a different voting power to each group. In spite of its effectiveness, though similarly to the competing methods in the literature, the proposed method may lead to more than one efficient candidate. Several ranking models are extended and used to discriminate among the efficient candidates. Despite the wealth of information provided to the decision maker(s), different extended ranking models may produce different rankings. Simulation is used to analyze these rankings and synthesize them into one overall group ranking. A case study is used to demonstrate the applicability and exhibit the efficacy of the proposed method.