Rank similarity based MADM method selection

Selecting the most suitable Multiple Attribute Decision Making (MADM) method for a given MADM problem is a challenge for the decision maker. When there are several suitable MADM methods available for the problem, the challenge is even greater. We present a novel MADM method selection approach based on the Spearman's rank correlation. The approach will help the decision maker in selecting the most preferred MADM method from a set of suitable and acceptable methods. The most preferred MADM method is the one that produces the most preferred outcome. The most preferred outcome is the one which is closest to all other outcomes. The closeness between the ranking outcomes are measured in terms of the similarity between them.