A new optimization model called robust stochastic optimization (RSO) has been developed to handle uncertainty in decision-making. This model combines different existing methods and can be easily solved using commercial optimization software. A new tool called Robust Stochastic Optimization Made Easy (RSOME) has also been created to help implement RSO models. The model can handle both discrete and continuous random variables and includes techniques from both stochastic linear optimization and distributionally robust optimization. The researchers have introduced event-wise recourse adaptations to address decision-making uncertainties. Various ambiguity sets have been proposed to capture different types of uncertainty. The study provides examples of RSO models, including optimizing using the Hurwicz criterion and two-stage problems over Wasserstein ambiguity sets.