Determination of Local and Global Decision Weights Based on Fuzzy Modeling

An essential challenge in multi-criteria decision analysis (MCDA) is the determination of criteria weights. These weights map the decision maker’s preferences for decision problems in determining the importance of criteria. However, these values are not necessarily constant in the whole domain. Although many approaches are related to their determination, some MCDA models can have local weights that are difficult to map in global spaces. This paper focuses on an approach in which we determine global and local weights from the Characteristic Objects METhod (COMET) by using linear regression. Moreover, obtained linear models are compared with COMET and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) models to answer how similar they are. Then, the relationships between the obtained global and local weights are analyzed based on a simple case study. The results demonstrate the high sensitivity of the COMET method and the applicability of the proposed approach for determining global and local weights. The most useful contribution is the proposed approach to identify local weights that can be used for deeper decision analysis.