Intuitive Multiple Centroid Defuzzification of Intuitionistic Z-Numbers

• Published in: Journal of Fuzzy Extension and Applications
• Main Author: Hakim Nik Badrul Alam N.M.F.; Ku Khalif K.M.N.; Jaini N.I.; Abu Bakar A.S.; Abdullah L.
• Format: Article
• Language: English
• Published: Research Expansion Alliance (REA) 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184463991&doi=10.22105%2fjfea.2022.315297.1173&partnerID=40&md5=0dd3f249398c253a008be522c2ac44ea

In fuzzy decision-making, incomplete information always leads to uncertain and partially reliable judgements. The emergence of fuzzy set theory helps decision-makers in handling uncertainty and vagueness when making judgements. Intuitionistic Fuzzy Numbers (IFN) measure the degree of uncertainty better than classical fuzzy numbers, while Z-numbers help to highlight the reliability of the judgements. Combining these two fuzzy numbers produces Intuitionistic Z-Numbers (IZN). Both restriction and reliability components are characterized by the membership and non-membership functions, exhibiting a degree of uncertainties that arise due to the lack of information when decision-makers are making preferences. Decision information in the form of IZN needs to be defuzzified during the decision-making process before the final preferences can be determined. This paper proposes an Intuitive Multiple Centroid (IMC) defuzzification of IZN. A novel Multi-Criteria Decision-Making (MCDM) model based on IZN is developed. The proposed MCDM model is implemented in a supplier selection problem for an automobile manufacturing company. An arithmetic averaging operator is used to aggregate the preferences of all decision-makers, and a ranking function based on centroid is used to rank the alternatives. The IZN play the role of representing the uncertainty of decision-makers, which finally determine the ranking of alternatives. © 2022, Research Expansion Alliance (REA). All rights reserved.