Optimization under uncertainty: theoretical, computational aspects and applications

Uncertainty is present in numerous real-world situations and directly affects decision-making processes. Understanding, modeling, and incorporating this uncertainty into optimization problems is essential to obtain more applicable solutions. In this short course, we will discuss the semantics of uncertainty, its formal definitions and mathematical implications, as well as present relevant theoretical results and different approaches to modeling and solving problems under uncertainty. The exhibition will be based on real applications extracted from projects in which the lecturers participated or coordinated, illustrating how these concepts materialize in concrete problems. Computational strategies for the implementation of the solutions will also be addressed, using the Python language and the AMPL mathematical modeling environment. The mini-course is aimed at students, researchers and professionals with an interest in optimization, who have basic knowledge of mathematical programming.