On scenario aggregation to approximate robust combinatorial optimization problems

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

zbMATH Keywords

min-max optimizationscenario aggregationapproximation algorithmsrobust combinatorial optimizationmin-max regret optimization

Mathematics Subject Classification ID

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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