A Novel Multi-Criteria Group Decision-Making Method for Heterogeneous Preference and Attribute Interaction Contexts

Heterogeneous preferences and attribute interaction are two important features in realistic multi-attribute group decision-making (MAGDM), directly affecting the decision quality. However, few MAGDM methods can handle both. This study proposes a novel MAGDM method suitable for heterogeneous preference and attribute interaction environments. First, a three-dimensional determinacy-uncertainty space method for homogenizing the heterogeneous preferences of real numbers, interval numbers, triangular fuzzy numbers, and triangular intuitionistic fuzzy numbers is proposed, avoiding information distortion and simplifying the complexity of the homogenized preference expression and model. Considering that the static weight method commonly used in existing MAGDM cannot fully reflect attributes importance variation for different alternatives, this study innovatively constructs a Shapley-based dynamic variable weight (SDVW) model to determine interaction attribute weight . Subsequently, the SDVW-Choquet integral is established to fuse the non-additive preferences, solving the compensation problem of non-additive preference fusion and simplifying the complexity of the fusion process. Finally, the case study and comparison analysis demonstrate the effectiveness, robustness, and advantages of the proposed method. The results show that simultaneously considering heterogeneous preferences and attribute interaction features can improve the accuracy and reliability of the MAGDM ranking results.