Improving the data normalization method in the CORASO method

The CORASO (COmpromise Ranking from Alternative SOlutions) method is one of the more recent MCDM (Multi-Criteria Decision-Making) methods, developed in 2024. It is a straightforward method that has been proven to be highly accurate in ranking alternatives. However, the method becomes unusable if a maximization-type criterion in the decision matrix has a maximum value of zero for a certain alternative, or if a minimization-type criterion has a value of zero for any alternative. This issue is directly related to the existing data normalization technique used within the CORASO method. This study was conducted to identify alternative data normalization methods that can be used with the CORASO method. Three specific data normalization techniques are investigated in this study: linear normalization method (the default method in CORASO), Weitendorf normalization method, and the vector normalization method. These three normalization methods were combined with the CORASO method to solve three different decision-making problems, each with varying numbers of alternatives to be ranked, as well as different numbers and types of criteria. The ranking results obtained using the CORASO method (when combined with the three aforementioned normalization methods) were compared with the results from other established MCDM methods. The findings confirmed that both the Weitendorf normalization and Vector normalization methods are suitable for use with the CORASO method. These two normalization methods were then applied in a specific case where the native CORASO normalization method could not be used. The results consistently demonstrated that these two normalization methods fully meet the requirements when integrated with the CORASO method