Expanding the Data Normalization Strategy to the MACONT Method for Multi-Criteria Decision Making

Abstract

The Mixed Aggregation by Comprehensive Normalization Technique (MACONT) is a well-known Multi-Criteria Decision-Making (MCDM) method with significant benefits compared to traditional approaches. The key difference that distinguishes this method from most others is the use of data normalization techniques and aggregation approaches. MACONT uses three different data normalization techniques simultaneously along with two aggregation approaches throughout its evaluation process. This reduces the derivation of evaluation values and enhances the reliability of the final decision results, making the process more precise and convergent. However, the original MACONT emphasizes the integration of multiple normalization techniques of the same type of criteria that might perform badly in some circumstances. This paper proposes combination strategies of six normalization techniques to be coupled with the MACONT to help the normalized data synthetically reflect the original information and solve different types of data, criteria, and alternatives. The proposed approach was applied in four case studies. In all studies, the ranking results were compared with the other MCDM methods, producing the same best alternatives and overcoming the cases when the original MACONT did not work properly.