EURO 2024 Copenhagen
Abstract Submission

2353. Robust Optimization Under Controllable Uncertainty

Invited abstract in session TD-4: Robust and Multi-Level Optimization, stream MINLP.

Tuesday, 14:30-16:00
Room: 1001 (building: 202)

Authors (first author is the speaker)

1. Eva Ley
Institute for Mathematical Optimization, TU Braunschweig
2. Rebecca Marx
Institut für Mathematische Optimierung, TU Braunschweig
3. Maximilian Merkert
Institute for Mathematical Optimization, TU Braunschweig
4. Tim Niemann
Institute for Mathematical Optimization, Technische Universität Braunschweig
5. Sebastian Stiller
TU Braunschweig

Abstract

Applications for optimization with uncertain data in practice often feature a possibility to reduce the uncertainty at a given query cost, e.g., by conducting measurements, surveys, or paying a third party in advance to limit the deviations. To model this type of applications, we introduce the concept of optimization problems under controllable uncertainty (OCU). For an OCU we assume the uncertain cost parameters to lie in bounded, closed intervals. The optimizer can shrink each of these intervals around a certain value called hedging point, possibly reducing it to a single point. Depending on whether the hedging points are known in advance or not, different types of OCU arise. Moreover, the models may differ with respect to when the narrowing down, the underlying optimization, and/or the revelation of true data take place.
In the talk, we discuss two example problem settings - one with known and one with unknown hedging points - in more detail, where we handle the remaining uncertainty by the paradigm of robust optimization. For both cases, we give conditions under which a single-level reformulation is possible. Throughout the talk, we use shortest-path problems as underlying optimization problem for illustrating specifics of and phenomena arising in OCU.

Keywords

Status: accepted

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