Optimization Under Uncertainty

This chapter discusses several aspects of relevance in optimization under uncertainty. Different paradigms are reviewed, such as Robust Optimization, Stochastic Programming, Chance-Constrained Programming, and Distributionally Robust Optimization. The reviewed concepts, models, and techniques seek to provide the reader with the background for better capturing the contents related to facility location to be presented in the remainder of the volume.