Seminar announcement: Dr. Rosario Paradiso (Vrije Universiteit Amsterdam) @DEI – University of Padova, October 04th, 2023
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Airo Young
Oct 3, 2023, 2:22:58 AM10/3/23
to airo-...@googlegroups.com, Roberto Roberti
Dear AIROYoungers,
We forward you the following seminar announcement in case you are interested.
Best,
the AIROYoung board
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Dear Colleagues,
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On Wednesday, October 4th, at 16:30, Dr. Rosario Paradiso, Assistant Professor at the Department of Operations Analytics of the Vrije Universiteit Amsterdam, is giving a seminar on the latest advancements in robust optimization with decision-dependent information discovery.
The title of the talk is "Exact and Approximate Schemes for Robust Optimization Problems with Decision Dependent Information Discovery".
Abstract:
Uncertain optimization problems with decision-dependent information discovery assume that the decision-maker can control the timing of information discovery, in contrast to the classic setting where uncertain parameters are revealed based on a prescribed filtration. In this work, we study two-stage robust optimization problems with decision-dependent information discovery where uncertainty appears in the objective function. We propose the first exact algorithm for this class of problems and improve upon the existing K-adaptability approximation by strengthening its formulation. The approaches are tested on robust orienteering and shortest-path problems. We demonstrate that the exact solution method outperforms the K-adaptability approximation and showcase the benefits of the strengthened K-adaptability formulation even in the decision-independent information discovery setting.
Uncertain optimization problems with decision-dependent information discovery assume that the decision-maker can control the timing of information discovery, in contrast to the classic setting where uncertain parameters are revealed based on a prescribed filtration. In this work, we study two-stage robust optimization problems with decision-dependent information discovery where uncertainty appears in the objective function. We propose the first exact algorithm for this class of problems and improve upon the existing K-adaptability approximation by strengthening its formulation. The approaches are tested on robust orienteering and shortest-path problems. We demonstrate that the exact solution method outperforms the K-adaptability approximation and showcase the benefits of the strengthened K-adaptability formulation even in the decision-independent information discovery setting.
The seminar will be held in person only at the Department of Information Engineering of the University of Padova, Meeting Room DEI/G 318.
Kind regards,
Roberto Roberti
AIROYoung
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