Mixed uncertainty sets for robust combinatorial optimization

Dokka, T. and Goerigk, M. and Roy, R. (2020) Mixed uncertainty sets for robust combinatorial optimization. Optimization Letters, 14. 1323–1337. ISSN 1862-4472

Text (paper-highlighted)
paper_highlighted.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (325kB)


In robust optimization, the uncertainty set is used to model all possible outcomes of uncertain parameters. In the classic setting, one assumes that this set is provided by the decision maker based on the data available to her. Only recently it has been recognized that the process of building useful uncertainty sets is in itself a challenging task that requires mathematical support. In this paper, we propose an approach to go beyond the classic setting, by assuming multiple uncertainty sets to be prepared, each with a weight showing the degree of belief that the set is a “true” model of uncertainty. We consider theoretical aspects of this approach and show that it is as easy to model as the classic setting. In an extensive computational study using a shortest path problem based on real-world data, we auto-tune uncertainty sets to the available data, and show that with regard to out-of-sample performance, the combination of multiple sets can give better results than each set on its own.

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/s11590-019-01456-3
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
28 Aug 2019 09:35
Last Modified:
22 Nov 2022 07:55