On scenario aggregation to approximate robust combinatorial optimization problems
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Publication:1800442
DOI10.1007/s11590-017-1206-xzbMath1407.90340arXiv1611.09754OpenAlexW2766241778MaRDI QIDQ1800442
Marc Goerigk, André B. Chassein
Publication date: 23 October 2018
Published in: Optimization Letters (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1611.09754
min-max optimizationscenario aggregationapproximation algorithmsrobust combinatorial optimizationmin-max regret optimization
Minimax problems in mathematical programming (90C47) Stochastic programming (90C15) Combinatorial optimization (90C27)
Related Items (6)
Approximating combinatorial optimization problems with the ordered weighted averaging criterion ⋮ Mixed uncertainty sets for robust combinatorial optimization ⋮ Optimal scenario reduction for one- and two-stage robust optimization with discrete uncertainty in the objective ⋮ Combinatorial optimization problems with balanced regret ⋮ Generating hard instances for robust combinatorial optimization ⋮ Representative scenario construction and preprocessing for robust combinatorial optimization problems
Cites Work
- A new bound for the midpoint solution in minmax regret optimization with an application to the robust shortest path problem
- On a constant factor approximation for minmax regret problems using a symmetry point scenario
- Approximation of min-max and min-max regret versions of some combinatorial optimization problems
- Min-max and min-max regret versions of combinatorial optimization problems: A survey
- An approximation algorithm for interval data minmax regret combinatorial optimization problems
- Robust discrete optimization and its applications
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