A fuzzy parametric model for decision making involving F-OWA operator with unknown weights environment

• Published in: Heliyon Vol. 9; no. 9; p. e19969
• Main Authors: Touqeer, Muhammad, Sulaie, Saleh Al, Lone, Showkat Ahmad, Shaheen, Kiran, Gunaime, Nevine M., Elkotb, Mohamed Abdelghany
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023; Elsevier
• Subjects: Fuzzy ordered weighted averaging (F-OWA); Linguistic variables; Maximizing deviation; Three trapezoidal fuzzy numbers (TTFNs); Linguistic variables; Maximizing deviation; Fuzzy ordered weighted averaging (F-OWA); Three trapezoidal fuzzy numbers (TTFNs)
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2023.e19969

Weight determining of attributes is an important factor in decision support systems since it corresponds to the relative importance of each criteria which is necessary to be determined since all the attributes aren't equally important. The aim of this paper is to put forward a method for multi Criteria decision making (MCDM) problems based on three trapezoidal fuzzy numbers under completely unknown weights environment. Based on the idea that the attribute with a larger deviation value among alternatives should be assigned a larger weight, an optimization model based on maximizing deviation method is established. F-OWA is considered to be vastly superior from the existing operators which usually take into account only the relative significance of decision makers. F-OWA operator considers not only the ratings of attribute values but also their ordered position that is it not only signifies the decision makers but also values the individual assessments. We utilize fuzzy ordered weighted averaging (F-OWA) operator to compute the collective overall preference value of each alternative and select the most desirable one according to their expected score values. The presented method is more generalized since we have used TTFNs, which are more effective in capturing uncertainty than IT2FS, just like triangular fuzzy numbers have a better representational power than simple interval numbers. Moreover, an illustrative example is given for the justification of the proposed technique.