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We propose a ranking method for fuzzy numbers.
This paper proposes a new ranking method for fuzzy numbers, which uses a defuzzification of fuzzy numbers and a weighting function.
For fuzzy numbers, the ellipsoid reduces simply to an ellipse.
Firstly, we present the basic concepts for fuzzy numbers and fuzzy-number-valued functions.
Many ranking methods for fuzzy numbers have been proposed so far.
Thus, non-fuzzy ranking method for fuzzy numbers needs to be employed for each criterion.
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One popular approach is based on H-differentiability for fuzzy number value functions.
Then, using a recently proposed distance function for comparison of fuzzy numbers, we present a dynamic programming method for finding a shortest path in the network.
For this, we apply a recently proposed distance function for comparison of fuzzy numbers.
We also give the following important characterization of statistical convergence for sequences of fuzzy numbers having multiplicity greater than two.
For sequences of fuzzy numbers, Savaş[25, 26] introduced and studied the concepts of strongly λ-summable λ-statistical convergence and asymptotically λ-statistical equivalent sequences, respectively.
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