INTERNATIONAL JOURNAL OF ADVANCE RESEARCH
IN MULTIDISCIPLINARY

In uncertain decision-making situations, the use of generalized hybrid fuzzy multiple centroids greatly improves the assessment of criteria and alternatives. This method combines a new defuzzification technique called intuitive multiple centroids with the fuzzy TOPSIS algorithm, which ranks solutions according to how close they are to the ideal. In uncertain contexts, traditional decision-making procedures that depend on accurate numerical facts typically fail. Alternatively, Type-2 fuzzy sets provide a more versatile way to mimic the intricacies of human decision-making by acting as useful instruments for approximation reasoning and uncertainty modeling. The fuzziness, ambiguity, and uncertainty that come with human judgments are beyond the capabilities of Type-1 fuzzy sets, which mainly deal with imprecision. This hybrid technique improves decision-making results in uncertain circumstances by combining intuitive multiple centroids with fuzzy TOPSIS. The result is a more robust selection framework.