Variable-sized uncertainty and inverse problems in robust optimization

André Chassein and Marc Goerigk

European Journal of Operational Research, 2018, vol. 264, issue 1, 17-28

Abstract: In robust optimization, the general aim is to find a solution that performs well over a set of possible parameter outcomes, the so-called uncertainty set. In this paper, we assume that the uncertainty size is not fixed, and instead aim at finding a set of robust solutions that covers all possible uncertainty set outcomes. We refer to these problems as robust optimization with variable-sized uncertainty. We discuss how to construct smallest possible sets of min–max robust solutions and give bounds on their size.

Keywords: Robustness and sensitivity analysis; Uncertainty sets; Inverse optimization; Optimization under uncertainty (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:264:y:2018:i:1:p:17-28

DOI: 10.1016/j.ejor.2017.06.042

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