Uncertainty is inherent in many decision and optimization problems for a variety of reasons: imperfect models, random inputs, noisy measurements of variables, and imprecise knowledge of dynamic parameters. Added to this is the frequent complexity of large contemporary systems. This axis groups together decision-making methods in complex and uncertain systems. These include state estimation, hierarchical optimal control, robust optimization, and mean field game theory.
Cahiers du GERAD
Within the context of optimization under uncertainty, a well-known alternative to minimizing expected value or the worst-case scenario consists in minimizing...BibTeX reference
Data-driven optimization with distributionally robust second-order stochastic dominance constraints
Optimization with stochastic dominance constraints has recently received an increasing amount of attention in the quantitative risk management literature. In...BibTeX reference
In the Weighted Fair Sequences Problem (WFSP), one aims to schedule a set of tasks or activities so that the maximum product between the largest temporal dis...BibTeX reference
Tryphon Georgiou – University of California at Irvine
Eilyan Bitar – Associate Professor, School of Electrical and Computer Engineering , Cornell University