Abstract
In modern industry, risk is often understood, communicated, and actioned upon through decision support tools. These tools often incorporate machine learning, statistical and optimization models. These models, especially optimization models, are often point estimate based and must be artfully massaged to incorporate uncertainty to build robust—risk adjusted models that provide a good basis for optimal, long term, goal-based decision making. Probability estimation, simulation and chance constrained optimization are three well known techniques that when used alone or in combination can increase the power of optimization models by considering the underlying risk of the process being optimized when recommending alternatives to decision makers.