A Novel Pythagorean Group Decision‐Making Method Based on Evidence Theory and Interactive Power Averaging Operator

Abstract

Since Pythagorean fuzzy sets can better reflect the cognition of the decision objects for experts, researchers have begun to pay increasingly more attention to them in recent years. The majority of the research on Pythagorean fuzzy environment assumes that the decision maker is completely rational and does not consider the correlation among the attribute variables. In view of the above, this paper proposes a method to solve the multiple attribute group decision-making problem based on D-S theory and interactive power averaging operator. First, the new Pythagorean fuzzy interactive weighted power average operator is designed to aggregate the attribute evaluation information given by experts one by one, and the comprehensive evaluation information of each expert is obtained. Then, the expert comprehensive evaluation information is aggregated by the rule of evidence combination to obtain the comprehensive evidence information and confidence interval of each candidate. Then, the decision-making method for candidate alternatives is performed by the possibility discriminant rule. The design method considers not only the decision makers’ bounded rationality but also the correlation among the attribute variables. Finally, the selection of the energy exploitation plan illustrates the feasibility and effectiveness of the proposed group decision method.

Enhanced Combined Techniques for Interval-Valued Intuitionistic Fuzzy Multiple-Attribute Group Decision-Making and Applications to Quality Evaluation of Large-Scale Multi-View 3D Reconstruction

“…Due to the complexity and uncertainty of the decision-making environment, it is difficult to obtain scientifically feasible decision results relying on a single decision expert [32][33][34][35][36]. In most cases, group decision-making (GDM) based on multiple decision experts is more efficient [37][38][39][40][41]. Therefore, group decision-making plays a very important role in daily life.…”

Conflict Evidence Fusion Algorithm Based on Cosine Distance and Information Entropy

“…However, the data collected by multiple sensors are not always consistent, and when faced with conflicting data, it is necessary to use D-S evidence theory for data fusion to obtain more accurate data. D-S evidence theory is widely applied in various fields of information fusion, such as decision making [1][2][3][4][5][6], pattern recognition [7][8][9], information fusion [10][11], supplier management [12][13][14], risk assessment [15][16], fault diagnosis [17][18] and so on [19][20].…”