In the modeling of many problems on linear optimization is not possible to consider the classic deterministic model because the set of parameters is not fully known due to the significant variation of the data along time or because there is no uniformity on the values. These kind of problems are known as problems with uncertainty and there are different approaches about modeling and methods of solution to resolve them. In this paper we make a review of such approaches focusing basically in stochastic optimization, fuzzy optimization, intervaling optimization and hybrid optimization. The difference between these approaches is perceived in the nature of the data, notions of feasibility and optimality and computational requirements, among others.

In the modeling of many problems on linear optimization is not possible to consider the classic deterministic model because the set of parameters is not fully known due to the significant variation of the data along time or because there is no uniformity on the values. These kind of problems are known as problems with uncertainty and there are different approaches about modeling and methods of solution to resolve them. In this paper we make a review of such approaches focusing basically in stochastic optimization, fuzzy optimization, intervaling optimization and hybrid optimization. The difference between these approaches is perceived in the nature of the data, notions of feasibility and optimality and computational requirements, among others.