Description: The stochastic optimization model is a mathematical approach that integrates randomness into the optimization process, allowing for the discovery of optimal solutions under conditions of uncertainty. Unlike deterministic models, which assume that all parameters are known and fixed, stochastic models recognize that many real-world problems are subject to variations and fluctuations. This type of model is characterized by the inclusion of random variables and the formulation of decisions aimed at maximizing or minimizing an objective function, considering the probabilities associated with different scenarios. The relevance of stochastic optimization models lies in their ability to provide more robust and realistic solutions in contexts where uncertainty is a critical factor. They are used in various fields, such as economics, engineering, logistics, finance, and operations research, where decisions must be made without knowing all the involved factors with certainty. In summary, these models allow decision-makers to evaluate different strategies and their possible outcomes, facilitating better risk and resource management in uncertain environments.
