Robust Optimization
Decision under uncertainty with imperfect probabilistic description of reality The field of decision-making under uncertainty was pioneered in the 1950s by Dantzig [6] and Charnes and Cooper [5], who set the foundation for, respectively, stochastic programming and optimization under probabilistic constraints. While these classes of problems require very different models and solution techniques, they share the same assumption that the probability distributions of the random variables are known exactly, and despite Scarf's [10] early observation that we may have reason to suspect that the future demand will come from a distribution that differs from that governing past history in an unpredictable way," the majority of the research efforts in decision-making under uncertainty over the past decades have relied on the precise knowledge of the underlying probabilities . Even under this simplifying assumption, a number of computational issues arises, e.g., the need for multi-variate ...