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For instance, the coefficients of stochastic differential equations can be modeled by fuzzy sets, and this leads to stochastic differential equations with fuzziness [20, 21].
Making use of the Transformation Method, the uncertain parameters of the system are modeled by fuzzy numbers in contrast to random numbers used in stochastic approaches.
These recommendations are computed through the characteristics of the competences from each subject and the designed activities, modeled by fuzzy linguistic labels.
This involves the separation of the hyper-spectral data into a predefined number of fuzzy groups, where each group covers a range of wavelengths and the contribution of each wavelength is modeled by fuzzy membership rules.
The vagueness of subjective human judgment on degree of application of lean practices is modeled by fuzzy number in conjunction with an additional consideration related to the length of lean practice implementation and the use of multi-evaluators.
To address real-world planning problems involving noisy, incomplete or erroneous data, the supplier capacity parameters of the supply chain and demand fluctuations are subject to uncertainty, which is modeled by fuzzy stochastic programming.
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To design the fuzzy controller, the chaotic system is modeled by Takagi Sugeno fuzzy system considering the properties of the nonlinear part of the system.
Peidro et al. (2009) proposed a fuzzy mixed integer linear programming model where data are ill known and modeled by triangular fuzzy numbers for supply chain planning under supply, process, and demand uncertainties.
In another research by Liu et al. (2010), component lifetime and repair time were modeled by random fuzzy exponential distribution.
The vague assessment "approximately between 4 to 6 days" is modeled by a fuzzy set (cf. Fig. 12).
The model has been formulated as a multi objective linear programming model where data are modeled by triangular fuzzy numbers.
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