Orlovsky's concept of decision-making with fuzzy preference relation—Further results
Fuzzy Sets and Systems
Preference relations on a set of fuzzy utilities as a basis for decision making
Fuzzy Sets and Systems
A procedure for ranking fuzzy numbers using fuzzy relations
Fuzzy Sets and Systems
Ranking fuzzy numbers with integral value
Fuzzy Sets and Systems
A new approach for ranking fuzzy numbers by distance method
Fuzzy Sets and Systems
Operations of fuzzy numbers with step form membership function using function principle
Information Sciences—Informatics and Computer Science: An International Journal
A model and algorithm of fuzzy product positioning
Information Sciences—Informatics and Computer Science: An International Journal
A context-dependent method for ordering fuzzy numbers using probabilites
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Mathematical Models in Engineering and Management Science
Fuzzy Mathematical Models in Engineering and Management Science
Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
IEEE Transactions on Fuzzy Systems
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In the field of engineering and economic studies, mathematical manipulation of fuzzy numbers is cumbersome and does not provide a neatly ordered set of results in the same way that crisp numbers do. Moreover, the generalized fuzzy number (i.e. non-normalized and normalized fuzzy number) have been approved more flexible and more intelligent than the normalized fuzzy number since it takes the degree of confidence of the decision-makers' opinions into account. In this paper the concept of the probability measure of fuzzy events is used to represent the fuzzy alternatives, and a ranking approach based upon their geometric moments are developed to make decision. The approach is computationally simple and its underlying concepts are logically sound. A fuzzy number with a superior geometric mean is ranked above fuzzy numbers having inferior geometric means, and in the case where the geometric means of two numbers happen to be equal, the number with a lower geometric variance is ranked above fuzzy numbers whose geometric variances are higher. A comparative study is conducted on cases used in the previous literatures to examine the performance of the proposed method on rationality and discriminatory ability.