Investigation of different normalization methods for TOPSIS

Normalizing the decision matrix is an important procedure in the technique for order preference by similarity to ideal solution (TOPSIS). The five well known normalization methods are investigated in this paper, including vector normalization method, max-min method, max/min method, sum method and exponent transformation method. Those methods were compared in terms of their ranking consistency index (RCI) when used with TOPSIS to solve the general multi-attribute decision making (MADM) problem with various problem sizes and data ranges. The higher the RCI is, the better normalization method. The results show that, among the five normalization methods, the vector normalization method is the best one for TOPSIS. It could deal with the general multi-attribute decision making (MADM) problems with various problem sizes, data ranges and attribute types effectively.