  We extend the classical risk minimization model with scalar risk measures to
the general case of set-valued risk measures. The problem we obtain is a
set-valued optimization model and we propose a goal programming-based approach
with satisfaction function to obtain a solution which represents the best
compromise between goals and the achievement levels. Numerical examples are
provided to illustrate how the method works in practical situations.
