94. Approximating Robust Problems by Uncertainty Sets
Invited abstract in session WB-2: Robust Optimization, stream Discrete and Combinatorial Optimization.
Wednesday, 10:45-12:15Room: H4
Authors (first author is the speaker)
| 1. | Marc Goerigk
|
| Business Decisions and Data Science, University of Passau | |
| 2. | André Chassein
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| Corporate Development, Deutsche Post DHL Group | |
| 3. | Jamie Fairbrother
|
| Department of Management Science, Lancaster University |
Abstract
For robust combinatorial optimization, a central problem ingredient that decides the problem complexity is the type of uncertainty set. Previous research has already discovered modifications to the uncertainty set as a path to derive approximation results by replacing high-cardinality discrete uncertainty sets by low-cardinality sets. In this talk, I discuss possibilities to replace one type of uncertainty set by another type of uncertainty set, including computational challenges of this approach.
Keywords
- Robust Optimization
- Approximation Algorithms
- Combinatorial Optimization
Status: accepted
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