Stochastické programovanie a jeho využitie v ekonomike

Abstract

This final thesis deals with the issue of stochastic programming, which is an important tool for solving decision problems in environments with uncertainty and randomness. The thesis will provide a general overview of probabilistic distributions and methods used in stochastic programming, with an emphasis on techniques such as L-Shaped and Base Decomposition. Specifically, the Monte Carlo approach, which is widely used to simulate random processes and estimate values in stochastic problems, will be explained. This approach will then be applied to address examples where its effectiveness and flexibility are demonstrated. In addition, examples solved by methods other than Monte Carlo will be presented in the work, using the normal probabilistic distribution to model the uncertainty and risk in these examples. This comprehensive view of stochastic programming aims to provide the reader with a comprehensive understanding of diverse methods and approaches to solving stochastic decision problems.