This paper is on fuzzy stochastic optimisation, an area that is quickly coming to the forefront of mathematical
programming under uncertainty. An even stronger motivating factor for the growing interest in
this area can be found in the ubiquitous nature of decision problems involving hybrid imprecision. More
precisely, we consider a range of situations in which random factors and fuzzy information co-occur in an
optimisation setting. Related hybrid optimisation models are discussed and converted into deterministic
terms through appropriate tools like probabilistic set, uncertain probability, and fuzzy random variable,
making good use of uncertainty principles. We also discuss ways to deal with the resulting problems.
Numerical examples carried out using class optimisation software demonstrate the efficiency of the proposed
approaches. We shall end this article by pointing out some of the challenges that currently occupy
researchers in this emerging field.