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In order to evaluate the robustness performance of process control system with uncertainties, four quantified stochastic robustness indices are introduced upon some typical stochastic variable ranking criteria. Based on them, four kinds of stochastic robust controller optimization problems are presented to meet the system requirements. To solve the multi-objective stochastic programming problems, NSGA-II algorithm combined with Monte-Carlo experiments are utilized to obtain the Pareto robustness solutions. The methods are applied to a steam-turbine generator set control system design. The simulation results demonstrate better robustness performance compared with those obtained under nominal parameter condition. Further more, detailed comparison among the four methods reveals their unique character, in which pessimistic value criterion method is considered to be relatively the best.