This paper reviews the state-of-the-art in optimization under uncertainty, discussing various models and methods used to address decision-making in the presence of uncertainty. It covers stochastic programming, robust stochastic programming, probabilistic programming, fuzzy programming, and stochastic dynamic programming. The paper highlights the challenges of dealing with uncertainty, particularly in large-scale optimization problems, and discusses the advantages and limitations of each approach. It also reviews recent developments in computational methods and applications across various fields such as production planning, scheduling, location, transportation, finance, and engineering design. The paper emphasizes the need for further research in areas such as polynomial-time approximation schemes for multi-stage stochastic programs and the application of global optimization algorithms to two-stage and chance-constraint formulations. It concludes by discussing the connections between global optimization and optimization under uncertainty, and the potential for future developments in this field.This paper reviews the state-of-the-art in optimization under uncertainty, discussing various models and methods used to address decision-making in the presence of uncertainty. It covers stochastic programming, robust stochastic programming, probabilistic programming, fuzzy programming, and stochastic dynamic programming. The paper highlights the challenges of dealing with uncertainty, particularly in large-scale optimization problems, and discusses the advantages and limitations of each approach. It also reviews recent developments in computational methods and applications across various fields such as production planning, scheduling, location, transportation, finance, and engineering design. The paper emphasizes the need for further research in areas such as polynomial-time approximation schemes for multi-stage stochastic programs and the application of global optimization algorithms to two-stage and chance-constraint formulations. It concludes by discussing the connections between global optimization and optimization under uncertainty, and the potential for future developments in this field.