Mitiku, Semu (PhD)Zerfu, Solomon2018-07-182023-11-042018-07-182023-11-042014-02http://etd.aau.edu.et/handle/123456789/9148Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inability to know the future. This paper focuses on types of Stochastic optimization such as Stochastic optimization problems with recourse and Chance constrained optimization problems as well as how to change one Stochastic optimization problems to deterministic equivalent form. Keywords: Probability, Random Variable, Expected value, Measure, Convex, Stochastic Optimization, Recourse, Chance ConstrainedenProbabilityRandom VariableExpected valueMeasureConvexStochastic OptimizationRecourseChance ConstrainedThesis