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Therefore, the use of fuzzy numbers in the context of this paper is deemed suitable.
Zadeh (1965) suggests the use of "fuzzy numbers" and "fuzzy clusters" in order to overcome the difficulty of accounting for the uncertainty caused by linguistic variables.
The use of fuzzy numbers (uncertain numbers) allows one to estimate the accuracy of the model's results even when the available data are not suitable for statistical analysis.
In the first method, the performance factors and the Analytic Hierarchy Process (AHP) were fuzzified and the use of fuzzy numbers and a fuzzy AHP for this problem was recommended.
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An algorithm that uses approximation of fuzzy numbers with a discrete family of intervals is proposed.
A membership function μ A ~ (x i ) gives the membership degree of a generic element x i to the fuzzy set A ~. Trapezoidal fuzzy numbers are the most widely used forms of fuzzy numbers because they can be handled arithmetically and interpreted intuitively.
Moreover, their models often use algebraic operations of fuzzy numbers to calculate the fuzzy sets in QFD.
This paper proposes a new ranking method for fuzzy numbers, which uses a defuzzification of fuzzy numbers and a weighting function.
Using the definition of fuzzy numbers, it follows that for any (alphain[0, 1]), ([u]^{alpha}) is a bounded closed interval.
This paper deals with the approximate controllability of impulsive neutral fuzzy stochastic differential equations with nonlocal conditions in a Banach space by using the concept of fuzzy numbers whose values are normal, convex, upper semicontinuous and compact.
In order to solve the problem, developed fuzzy model is converted to a crisp model by using expected values of fuzzy numbers and taking into account the preference of decision maker regarding cost and reliability goals.
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