In mathematical optimization models, we commonly assume that the data inputs are precisely known and ignore the in°uence of parameter uncertainties on the optimality and feasibility of the models. It is therefore conceivable that as the data di®ers from the assumed nominal values, the generated \optimal solution" may violate critical constraints and perform poorly from an objective function point of view. These ob- servations motivate the need for methodologies in mathematical optimization models that solve for solutions that are immune to data uncertainty