  We re-examine a practical aspect of combinatorial fuzzy problems of various
types, including search, counting, optimization, and decision problems. We are
focused only on those fuzzy problems that take series of fuzzy input objects
and produce fuzzy values. To solve such problems efficiently, we design fast
fuzzy algorithms, which are modeled by polynomial-time deterministic fuzzy
Turing machines equipped with read-only auxiliary tapes and write-only output
tapes and also modeled by polynomial-size fuzzy circuits composed of fuzzy
gates. We also introduce fuzzy proof verification systems to model the
fuzzification of nondeterminism. Those models help us identify four complexity
classes: Fuzzy-FPA of fuzzy functions, Fuzzy-PA and Fuzzy-NPA of fuzzy decision
problems, and Fuzzy-NPAO of fuzzy optimization problems. Based on a relative
approximation scheme targeting fuzzy membership degree, we formulate two
notions of "reducibility" in order to compare the computational complexity of
two fuzzy problems. These reducibility notions make it possible to locate the
most difficult fuzzy problems in Fuzzy-NPA and in Fuzzy-NPAO.
