Stochastic programming involves defining a objective function that needs to be optimized and a set of constraints. These elements are modeled with random variables to capture the uncertainty. The solution process typically involves generating multiple scenarios, each representing a possible state of the world, and finding a strategy that performs well across all scenarios.
