Informatica

In practical linguistic multi-criteria group decision-making (MCGDM) problems, words may indicate different meanings for various decision makers (DMs), and a high level of group consensus indicates that most of the group members are satisfied with the final solution. This study aims at developing a novel framework that considers the personalized individual semantics (PISs) and group consensus of DMs to tackle linguistic single-valued neutrosophic MCGDM problems. First, a novel discrimination measure for linguistic single-valued neutrosophic numbers (LSVNNs) is proposed, based on which a discrimination-based optimization model is built to assign personalized numerical scales (PNSs). Second, an extended consensus-based optimization model is constructed to identify the weights of DMs considering the group consensus. Then, the overall evaluations of all the alternatives are obtained based on the LSVNN aggregation operator to identify the ranking of alternatives. Finally, the results of the illustrative example, sensitivity and comparative analysis are presented to verify the feasibility and effectiveness of the proposed method.

The purpose of this study is to develop a hesitant trapezoidal fuzzy TODIM (interactive and multi-criteria decision making) with a closeness index-based ranking method to handle hesitant qualitative group decision making problems. First, a novel closeness index-based ranking method is presented to compare the magnitude of hesitant trapezoidal fuzzy numbers (HTrFNs). Based on the developed ranking method, the dominance values of alternatives over others for each expert are calculated. Then, a nonlinear programming model is established to derive the dominance values of alternatives over others for the group and correspondingly the optimal ranking order of alternatives is obtained.