• A complementary methodology to stochastic programming and sensitivity analysis
• Seeks a solution that will have an “acceptable” performance under most realizations of the uncertain inputs
• Usually, no distribution assumption is made on uncertain parameters (if such information is available, it can be utilized
beneficially)
• Usually, it is a conservative (worst-case oriented) methodology.