A simulation comparison of normalization procedures for TOPSIS

Abstract

Multiattribute decision making (MADM) uses a normalization procedure to transform performance ratings with different data measurement units in a decision matrix into a compatible unit. MADM methods generally use one particular normalization procedure without justifying its suitability. The technique for order preference by similarity to ideal solution (TOPSIS) is one of the most popular and widely applied MADM methods. This study compares four commonly known normalization procedures in terms of their ranking consistency and weight sensitivity when used with TOPSIS to solve the general MADM problem with various decision settings. The comparison study is validated using two performance measures: ranking consistency and weight sensitivity. A large number of MADM problems with varying attributes and alternatives are generated using a new simulation technique. The study results justify the use of the vector normalization procedure for TOPSIS and provide suggestive insights for using other normalization procedures in certain decision settings.