Comparison of multi-criteria decision-making methods with the same normalization procedure based on real-life applications

The ranking of a set of objects defined by a single data set may vary due to differences in multi-criteria decision-making (MCDM) procedures. One of these procedural differences is normalization, which is an important step in data analysis and MCDM methods. In terms of demonstrating the impact of the normalization process on the results, this study aims to compare MCDM methods with a linear normalization process. This study works on eight ranking methods (WASPAS, SECA, SAW, OWA, CODAS, MARCOS, PSI, and WPM), and three weighting methods (Entropy, EW, LOPCOW) based on three reallife applications. The study primarily explains the differences in rankings by the MCDM methods. Additionally, it is also important to demonstrate the impact of different weights on the results. The study found that the MCDM rankings obtained with the same normalization process differed, and it also observed that different criterion weights had an impact on the ranking results. This study contributes to the literature as it is the first to compare MCDM methods using linear normalization processes based on real-life applications.