Abstract
This paper extends the applicability of optimization models to situations where both fuzzy and random data are in the state of affairs. The major mathematical tool used to this end is the concept of fuzzy random variables, which provides a synergistic way for dealing with the two kinds of imprecision. The extension principle is canonically extended to fuzzy random variables and exploited to derive a way for handling robust fuzzy stochastic mathematical programming problems. Finally, we provide some possible avenues for further fruitful developments in this field.
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