RANK REVERSAL IN TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION – TOPSIS
Rank Reversal; Rank Inconsistency; TOPSIS; Multi-Criteria Decision-Making.
During the last decades, various multi-criteria decision-making methods (MCDM) have been used to assist decision makers in selecting the best alternatives for many decision problems. Among them, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of the most used. Despite its wide dissemination, it has been criticized due to the occurrence of a problem called rank reversal, which in its most known meaning refers to the change in the ordering of a group of previously ordered alternatives after an irrelevant alternative has been added or removed from this group. Despite the significant amount of research on this problem for MCDM methods, it has been superficially analyzed in the case of TOPSIS, without a careful study on the occurrence causes and conditions, as well as marked by propositions inadequate models. Because of this, the aim of this study is to analyze the problem of rank reversal in TOPSIS in order to propose solutions to minimize it. For this, it was realised an experimental research through computer simulations randomly generated based on four reversal criteria selected in the literature. Preliminary results showed that the TOPSIS has lower rates of reversal when is used a linear transformation standardization of scale by the maximum and a indifference limit of 15% in the proximity coefficients.