With the increasing complexity of engineered systems and the prevalence of uncertainty, design methodologies have progressed beyond traditional deterministic frameworks. This workshop convenes researchers and practitioners to discuss advances of optimization methods aimed at enhancing the robustness of general high-dimensional, nonlinear systems. This includes robust design approaches with given uncertainty or perturbation model, and tolerance optimization approaches that maximize permissible uncertainty or perturbation. Areas of application include, but are not limited to structural/fluid/solid mechanics, thermal systems, control systems and mechatronics. Methods to address uncertainty within the design optimization may include all sorts of probabilistic approaches, such as surrogate-based Monte Carlo methods or Taylor expansion-based approximations, embedded in reliability-based or robust design optimization, or stochastic gradient approaches. Alternative methods to handle uncertainty, such as Fuzzy set theory, solution spaces, interval analysis or worst-case approaches are also encouraged to be submitted.