Stochastic programming is a mathematical optimization technique that deals with decision-making under uncertainty. It involves solving optimization problems in which some or all of the parameters are not known with certainty, but are instead modeled as random variables with known probability distributions. The goal of stochastic programming is to find optimal decisions that minimize or maximize some objective function, taking into account the uncertainty in the problem parameters. Stochastic programming has applications in a wide range of fields, including finance, energy systems, supply chain management, and transportation. It is particularly useful in situations where decision-makers need to make decisions in the face of uncertainty, and where the consequences of those decisions are affected by random factors. There are several different approaches to solving stochastic programming problems, including stochastic linear programming, stochastic integer programming, and stochastic dynamic programming. Each of these methods involves different techniques for modeling uncertainty, defining decision variables, and solving the optimization problem. Overall, stochastic programming provides a powerful framework for making robust and optimal decisions in the presence of uncertainty.