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Min-max-min Robust Combinatorial Optimization Subject to Discrete Uncertainty

Christoph Buchheim (christoph.buchheim***at***math.tu-dortmund.de)
Jannis Kurtz (jannis.kurtz***at***math.tu-dortmund.de)

Abstract: We consider combinatorial optimization problems with uncertain objective functions. In the min-max-min robust optimization approach, a fixed number k of feasible solutions is computed such that the respective best of them is optimal in the worst case. The idea is to calculate a set of candidate solutions in a potentially expensive preprocessing and then select the best solution out of this set in real-time, once the actual scenario is known. In this paper, we investigate the complexity of this min-max-min problem in the case of discrete uncertainty, as well as its connection to the classical min-max robust counterpart. Essentially, it turns out that the min-max-min problem is not harder to solve than the min-max problem, while producing much better solutions in general.

Keywords: Robust Optimization, k-Adaptability, Discrete Uncertainty

Category 1: Robust Optimization

Category 2: Combinatorial Optimization

Category 3: Integer Programming (0-1 Programming )

Citation:

Download: [PDF]

Entry Submitted: 02/01/2016
Entry Accepted: 02/01/2016
Entry Last Modified: 02/01/2016

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