Faster algorithms for min-max-min robustness for combinatorial problems with budgeted uncertainty
Year of publication: |
2019
|
---|---|
Authors: | Chassein, André ; Goerigk, Marc ; Kurtz, Jannis ; Poss, Michael |
Published in: |
European journal of operational research : EJOR. - Amsterdam : Elsevier, ISSN 0377-2217, ZDB-ID 243003-4. - Vol. 279.2019, 2 (1.12.), p. 308-319
|
Subject: | Combinatorial optimization | Robust optimization | k-adaptability | Budgeted uncertainty | Branch-and-bound algorithms | Algorithmus | Algorithm | Theorie | Theory | Mathematische Optimierung | Mathematical programming | Robustes Verfahren | Robust statistics | Scheduling-Verfahren | Scheduling problem | Risiko | Risk | Branch-and-Bound | Branch and Bound |
-
Robust combinatorial optimization under convex and discrete cost uncertainty
Buchheim, Christoph, (2018)
-
Robust combinatorial optimization with variable cost uncertainty
Poss, Michael, (2014)
-
On recoverable and two-stage robust selection problems with budgeted uncertainty
Chassein, André, (2018)
- More ...
-
Data-driven robust optimization using deep neural networks
Goerigk, Marc, (2023)
-
Data-driven prediction of relevant scenarios for robust combinatorial optimization
Goerigk, Marc, (2025)
-
A bicriteria approach to robust optimization
Chassein, André, (2016)
- More ...