Optimization under Uncertainty through Problem Reformulations
G. Veviurko (TU Delft - Algorithmics)
M. Weerdt – Promotor (TU Delft - Algorithmics)
Wendelin Böhmer – Copromotor (TU Delft - Sequential Decision Making)
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Abstract
The research in this thesis falls within the realm of optimization under uncertainty, a crucial area in computer science and mathematics with broad applications in power systems, finance, machine learning, healthcare, and more. This thesis presents three main contributions across electric vehicle charging scheduling, decision-focused learning, and reinforcement learning. Beyond advancing the state of the art in each of these domains, our contributions emphasize the importance of effective problem formulations