A new DEA approach to rank alternatives in MCDA

Abstract

One of the principal subjects in multiple criteria decision analysis is ranking alternatives. Here, we present a new method to rank alternatives by using data envelopment analysis. In this paper, one ranking method is proposed based on applying an artificial alternative called aggregate alternative. The method is based on the fact that one efficient alternative with a better performance has stronger effects on the group of other alternatives. That means its deletion forces the remaining alternatives to get smaller efficiency. The described idea in this paper is inspired of Lotfi and et al. (2011). One feature of the proposed method is that it does not need to determine the weight of the prior. Two examples are used to illustrate how the proposed method works in actual practices, and the results are compared with those obtained from the TOPSIS method.

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“…The common weights structure of the proposed model has more discriminatory power than those obtained by previous DEA-like models (Hatefi and Torabi, 2010). In order to overcome the limitations of the model, new proposals have been developed (Sanei et al, 2011;Darehmiraki and Behdani, 2013;Hatefi and Torabi, 2018).…”