A Novel Intuitionistic Mapping Aggregation Model to Determine Expert Weight for Multiple Attribute Group Decision Making

Abstract

The purpose of this paper is to develop a novel approach for predicting or completing expert weights in multiple attribute group decision making with intuitionistic fuzzy information. Firstly, optimal preference aggregation model is established for describing relationship between expert preferences. The experts' preferences are mapping as preference points with equal weight into two-dimensional space, and expert preferences for same attribute of same alternative are belong to a point set. Then, optimal aggregation preference point whose sum of Euclidean distance between expert preference points is shortest is calculated by plant growth simulation algorithm (PGSA). The closer preference information is to optimal aggregation preference point, higher expert weight is, and vice versa. Finally, in order to avoid falling into trap of the minority is subordinate to majority, final expert weight is obtained by modifying predicted expert weight with iterative correction algorithm. Compared with other algorithms, proposed method is more accurate to supplement missing expert weight information. The effectiveness and superiority of proposed method are illustrated by a typical example.