Assessing Normalization Techniques for TOPSIS Method

In recent years, data normalization is receiving considerable attention due to its essential role in decision problems. Especially, considering the new developments in Big data and Artificial Intelligent to handle heterogeneous data from sensors, normalization’s role as a preprocessing step for complex decision problems is more distinguished. However, selecting the best normalization technique among several introduced techniques in the literature is still an open issue. In this study we focus on evaluating normalization techniques in Multi-Criteria Decision Making (MCDM) methods namely for Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to recommend the most proper technique. A small numerical example, borrowed from literature, is used to show the applicability of the proposed assessment framework using several metrics for recommending the most suitable technique. This study helps decision makers to improve the accuracy of the final ranking of results in decision problems by selecting the best normalization technique for the related case study.