I would say that it occur most of the time. Let say a surveyor ask you the amount of population in a state, then you answer 3 million. For fuzzy type 1, you have to answer how certain you are that the number is 3 million, you might answer 50% or 0.5. For fuzzy type 2, you will be asked again how certain you are that you are 0.5 certain that the value is 3 million. It can go on forever and that's why the concept is generalized to fuzzy type n.

The choice of type 2 is because it is still easy to visualized the uncertainties. Once n>=3, the visualization is difficult.

Hi Dharmadas Mardanya,

I'd say the transportation problem because it involves layers of uncertain factors or decision-making in the process flow of the optimal transport economics and the allocation of resources. For example, the real-time traffic, GPS info accuracy, peak & off-peak hours (they are not binary logic).

I think Dr. Norazrizal Aswad Abdul Rahman has given you a simple-to-understand illustration of the type-n fuzziness.

Excellent answer of Yew-Chung Chak

Ok, there are mainly three including triangular in assessment, MCDM, Evaluations and others to mention but a few

Article The trapezoidal fuzzy soft set and its application in MCDM

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Article Use of the Triangular Fuzzy Numbers for Student Assessment