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Given a multicriteria decision‐making problem, an obvious question emerges: Which method should be used to solve it? Although some efforts had been made, the question remains open. The aim of this contribution is to compare a set of multicriteria decision‐making methods sharing three features: same fuzzy information as input data, the need of a data normalization procedure, and quite similar information processing. We analyze the rankings produced by fuzzy MULTIMOORA, fuzzy TOPSIS (with two normalizations), fuzzy VIKOR, and fuzzy WASPAS with different parameterizations, over 1200 randomly generated decision problems. The results clearly show their similarities and differences, the impact of the parameters settings, and how the methods can be clustered, thus providing some guidelines for their selection and usage.

Rank reversal is a common phenomenon in multi-criteria decision-making methods. It appears when the addition/deletion of new options to the alternatives’ set produces a change in the original ranking. In this contribution, we want to assess this phenomenon in the context of the VIKOR method. Using randomly generated multi-criteria decision problems, we confirmed that rank reversal existed and strongly depended on VIKOR’s parameter. Also, we observed that the influence of the number of alternatives was stronger than that of the number of criteria. Finally, although rank reversal may exist, we saw that it may not affect the top alternative of the ranking, thus potentially having a low impact.

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