Two New Eigenvector-based Approaches to Assign Weights to Decision Makers in Group Decision Making under Multiple Criteria

In this paper, we investigated the problem of weight determination among decision makers (DMs) in group decision-making process by extending two algorithms taken from the Eigenvector method. So, an expert (or DM) who consistently makes more accurate decisions relative to other DMs would be assigned a higher importance weight. In other words, the consistent DM, i) which has a minimum deviation between initial (or first iteration) and convergence (or final iterations) [in short, MDIC] obtained values, and/ or ii) minimum deviation between initial value given by a DM (aij) and the ratio of the derived priority vector (wi/ wj) [in short, MDIR], must have a greater weight, and vice-versa. The findings in this paper confirm the effectiveness of the proposed method. While, 0.339, 0.185, and 0.476; 0.373, 0.299, and 0.328 are the results obtained from application of proposed approaches, conventional method (the equal weights rule) assign 0.333, 0.333, and 0.333, respectively. In general, inconsistent experts get less weight. Finally, two numerical examples are given to demonstrate the feasibility of the developed method.