Novel Method for Ranking Generalized Fuzzy Numbers Based on Normalized Height Coefficient and Benefit and Cost Areas

Abstract

This paper proposes a method for ranking generalized fuzzy numbers, which guarantees that both horizontal and vertical values are important parameters affecting the final ranking score. In this method, the normalized height coefficient is introduced to evaluate the influence of the height of fuzzy numbers on the final ranking score. The higher the normalized height coefficient of a generalized fuzzy number is, the higher its ranking. The left and right areas are presented to calculate the impact of the vertical value on the final ranking score. The left area is considered the benefit area. The right area is considered the cost area. A generalized fuzzy number is preferred if the benefit area is larger and the cost area is smaller. The proposed method can be employed to rank both normal and non-normal fuzzy numbers without normalization or height minimization. Numerical examples and comparisons with other methods highlight the feasibility and robustness of the proposed method, which can overcome the shortcomings of some existing methods and can support decision-makers in selecting the best alternative.

Fuzzy risk analysis based on ranking generalized trapezoidal fuzzy numbers with different left-right heights by defuzzification using the volume of the solid of revolution about the axis

“…A new ranking principle based on a geometric approach to solve decisionmaking problems by calculating the distance between the centroids and midpoints of diagonals of GTrFNs by Bihari et al (2023). Le and Chu (2023) proposed a novel technique for ranking GFNs based on the normalized height coefficient and benefit and cost areas which considers maximizing and minimizing sets, the left area as the benefit area and the right area as the cost area.…”