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The technique for order preference by similarity to ideal solution (TOPSIS) is one of the most popular approaches for multiple criteria decision making (MCDM). The main limitation of the traditional TOPSIS lies in the inability to handle the ambiguity in the decision making process. Several researchers have introduced various fuzzy TOPSIS models. However, there is the key shortcoming in all previous approaches. When dealing with real information, fuzziness is not adequate and a degree of reliability of the information is very critical. In view of this, Prof. Zadeh introduced a Z-number for a more efficient explanation of real-life information. Compared with the usual fuzzy number, Z-number has extra capacity to depict the imperfect information. In this paper, Z-numbers is applied to express the relative weights of criteria and the fuzzy TOPSIS is used to rank the alternatives. The basic benefit of the proposed method is its low computational intricacy.
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Anvari et al. (2015) studied this question.
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