The
last three decades have witnessed development of optimization under fuzziness and randomness
also called Fuzzy Stochastic Optimization. The main objective of this new field is the need
for basing many human decisions on information which is both fuzzily imprecise
and probabilistically uncertain. Consistency indexes providing a union nexus
between possibilities and probabilities of uncertain events exist in the
literature. Nevertheless, there are no reliable transformations between them.
This calls for new paradigms for coping with mathematical models involving both
fuzziness and randomness. Fuzzy Stochastic Optimization (FSO) is an attempt to
fulfill this need. In this paper, we present a panoramic view of Fuzzy
Stochastic Optimization emphasizing the methodological aspects. The merits of
existing methods are also briefly discussed along with some related theoretical
aspects.
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