Fuzzy Sets and Systems
Ranking fuzzy numbers with integral value
Fuzzy Sets and Systems
Fuzzy system reliability analysis by interval of confidence
Fuzzy Sets and Systems
A new approach for ranking fuzzy numbers by distance method
Fuzzy Sets and Systems
A methodology of determining aggregated importance of engineering characteristics in QFD
Computers and Industrial Engineering
The revised method of ranking fuzzy numbers with an area between the centroid and original points
Computers & Mathematics with Applications
Ranking L-R fuzzy number based on deviation degree
Information Sciences: an International Journal
Area ranking of fuzzy numbers based on positive and negative ideal points
Computers & Mathematics with Applications
The revised method of ranking LR fuzzy number based on deviation degree
Expert Systems with Applications: An International Journal
Analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Ranking fuzzy numbers based on the areas on the left and the right sides of fuzzy number
Computers & Mathematics with Applications
A new approach for ranking of L-R type generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of weighting functions to the ranking of fuzzy numbers
Computers & Mathematics with Applications
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Ranking fuzzy numbers based on their left and right deviation degree (L-R deviation degree) has attracted the attention of many scholars recently, yet most of their ranking methods have two systematic shortcomings that are usually ignored. This paper addresses these shortcomings and proves them through mathematical proofs instead of providing counter-examples. Applying our analyses will help other authors avoid some common errors when building their own ranking index functions. We use Asady's ranking index function (2010) as an example when we present our arguments and proofs and provide fully detailed analyses of two of the ranking index functions herein. Based on these analyses, an algorithm for detecting inconsistencies in ranking results is proposed, and numerical examples are given to illustrate our arguments.