In this article, complicated group decision-making situations where the preference data is represented by linguistic variables are addressed using the dynamic programming approach. Making conclusions clear through accurate figures is difficult for decision-makers due to the complexity and ambiguity of reality. Neutrosophic is used to encode the linguistic variables because they cannot be directly computed. Neutrosophic sets are used to manage indeterminacy in a practical situation. The relationships between single and interval Neutrosophic sets are then measured using novel distance and similarity models. The suggested dynamic programming interval-based clustering methodology is then used to group the decision-makers. Additionally, a novel method for computing the interval weights of decision-makers and clusters is described, accounting for both the cluster center and group size. A centroid-based ranking system is then used to compare and order the possibilities, and illustrated experiments are presented to demonstrate how effectively the suggested technique operates. Comparisons and discussions are also done to show its superiority.
ابرومي سعيد | Broumi Said | 1039