Robust optimization is a branch of optimization theory that focuses on finding solutions that are resilient to uncertainty and variability in input parameters. It seeks to develop models and algorithms that can withstand fluctuations in data and variables, ensuring that the optimized solution remains feasible and effective in the face of uncertainty. Robust optimization differs from traditional optimization approaches in that it explicitly considers the potential variability and uncertainty in the input parameters, instead of assuming that these parameters are fixed and known with certainty. This can help decision-makers make more informed and reliable choices, especially in complex and uncertain environments. Overall, robust optimization aims to find solutions that perform well under a wide range of scenarios and conditions, providing a more robust and reliable approach to decision-making in complex systems.