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2947. Open Problems in Robust Combinatorial Optimization
Invited abstract in session TD-34: Trends and Open Problems in Robust Optimization, stream Stochastic, Robust and Distributionally Robust Optimization.
Tuesday, 14:30-16:00Room: 43 (building: 303A)
Authors (first author is the speaker)
| 1. | Marc Goerigk
|
| Business Decisions and Data Science, University of Passau | |
| 2. | Michael Hartisch
|
| Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg |
Abstract
Robust combinatorial optimization has come a long way over the last 25 years. While many variants of decision criteria and uncertainty sets have been proposed, some cornerstones have emerged. These are min-max, min-max regret, two-stage, and recoverable robust problems with discrete, interval, continuous budgeted, and discrete budgeted uncertainty. In this talk, I take stock of the current state-of-the-art in these areas and point out the challenges that we face. This includes a list of promising open problems that I encourage the audience to consider.
Keywords
- Robust Optimization
- Combinatorial Optimization
- Complexity and Approximation
Status: accepted
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