Stochastic programming

Stochastic programming is a framework for modeling optimization problems that involve uncertainty. Unlike traditional deterministic optimization, where the parameters of the model (such as costs, demands, or resource availabilities) are known with certainty, stochastic programming accounts for uncertainty by incorporating random variables and probabilistic constraints. The main idea is to make decisions that are robust against various possible future scenarios, allowing decision-makers to optimize an objective function while taking into consideration the risks and uncertainties inherent in the problem.