Aharon Ben-Tal and Arkadi Nemirovski introduced Robust Optimization (RO), a methodology for handling optimization problems with uncertain data. RO ensures that solutions remain feasible under all possible realizations of the uncertainty. The paper surveys RO applications in uncertain linear, conic quadratic, and semidefinite programming. For these cases, computationally tractable robust counterparts are derived or approximated, making RO useful for real-world applications. Applications include antenna design, truss topology design, and stability analysis in uncertain systems. A case study of 90 LPs from the NETLIB collection shows that small data perturbations can severely affect solutions, and RO can mitigate this. RO is particularly effective when uncertainty sets are ellipsoids. The paper discusses robust counterparts for linear, quadratic, and semidefinite programming, showing how RO can handle interval and ellipsoidal uncertainties. RO is shown to be effective in engineering design and control systems, providing robust solutions that are less sensitive to data variations. The methodology is computationally tractable and applicable to a wide range of problems, including those with complex uncertainty structures.Aharon Ben-Tal and Arkadi Nemirovski introduced Robust Optimization (RO), a methodology for handling optimization problems with uncertain data. RO ensures that solutions remain feasible under all possible realizations of the uncertainty. The paper surveys RO applications in uncertain linear, conic quadratic, and semidefinite programming. For these cases, computationally tractable robust counterparts are derived or approximated, making RO useful for real-world applications. Applications include antenna design, truss topology design, and stability analysis in uncertain systems. A case study of 90 LPs from the NETLIB collection shows that small data perturbations can severely affect solutions, and RO can mitigate this. RO is particularly effective when uncertainty sets are ellipsoids. The paper discusses robust counterparts for linear, quadratic, and semidefinite programming, showing how RO can handle interval and ellipsoidal uncertainties. RO is shown to be effective in engineering design and control systems, providing robust solutions that are less sensitive to data variations. The methodology is computationally tractable and applicable to a wide range of problems, including those with complex uncertainty structures.