  We present an standard constraints generation algorithm to find an explicit
set whose robustness is equal to the robustness of the feasible solution set of
a combinatorial optimization problem with cost uncertainty. Computational
experience shows that for problems with moderate dimensions the running time of
the algorithm may be tolerable and in many cases the number of solutions
required to achieve near-ideal robustness may be manageable for decision
makers.
