Abstract
The m-polar fuzzy set is the extended form of the bipolar fuzzy set. It is helpful in multi-criteria group decision-making (MCGDM). While solving problems with actual values of various criteria, normalization methods are used to make consistent data. Uniform data is input to multi-criteria decision-making (MCDM) methods. The single-valued m-polar fuzzy ELECTRE-I algorithm also uses normalized data as input parameters. There are different methods of normalization, which can be used to make data uniform. In this paper, with the help of the robot selection problem, we study the effect of five different normalization methods on the rank performance of the single-valued m-polar fuzzy ELECTRE-I algorithm. The m-polar fuzzy ELECTRE-I algorithm is beneficial in finding outranking relations between two alternatives. However, normalization methods affect outranking relations of other options and ultimately affect rank performance in the selection process. This study concluded in the recommendation of a suitable normalization method for the single-valued m-polar fuzzy ELECTRE-I method. It is supposed that linear sum (N3) and vector normalization (N4) methods are ideal for the m-polar fuzzy ELECTRE-I process to achieve consistent rank performance.
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