SELECTION OF SUITABLE DATA NORMALIZATION METHOD TO COMBINE WITH THE CRADIS METHOD FOR MAKING MULTI-CRITERIA DECISION

Abstract

Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) is a new MCDM method (discovered in 2022). It is built on a combination of three well-known methods, including Additive Ratio Assessment (ARAS), Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This method has the advantage of being resistant to the rank inversion phenomenon. However, if only the available data normalization (DN) method in this method is used, this method will only be usable in some cases. This study investigated the suitability of twelve data normalization methods combined with the CRADIS method. The solutions in four cases of four different fields were ranked using these twelve combination methods. Using these methods, the ranked results were compared with those of other MCDM methods. Four DN methods were appropriate in combination with the CRADIS method. The application scope of CRADIS method can be extended when using this DN method.

Keywords

• mcdm
• cradis method
• data normalization
• suitable dnm
• weight method