Representative scenario construction and preprocessing for robust combinatorial optimization problems

Goerigk, Marc and Hughes, Martin (2018) Representative scenario construction and preprocessing for robust combinatorial optimization problems. Optimization Letters. ISSN 1862-4472

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Abstract

In robust combinatorial optimization with discrete uncertainty, approximation algorithms based on constructing a single scenario representing the whole uncertainty set are frequently used. One is the midpoint method, which uses the average case scenario. It is known to be an N-approximation, where N is the number of scenarios. In this paper, we present a linear program to construct a representative scenario for the uncertainty set, which gives an approximation guarantee that is at least as good as for previous methods. We further employ hyper heuristic techniques operating over a space of preprocessing and aggregation steps to evolve algorithms that construct alternative representative single scenarios for the uncertainty set. In numerical experiments on the selection problem we demonstrate that our approaches can improve the approximation guarantee of the midpoint approach by more than 20%.

Item Type:
Journal Article
Journal or Publication Title:
Optimization Letters
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s11590-018-1348-5
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2606
Subjects:
ID Code:
128945
Deposited By:
Deposited On:
13 Nov 2018 14:36
Refereed?:
Yes
Published?:
Published
Last Modified:
26 May 2020 07:23