Generating hard instances for robust combinatorial optimization

Goerigk, M. and Maher, S.J. (2020) Generating hard instances for robust combinatorial optimization. European Journal of Operational Research, 280 (1). pp. 34-45. ISSN 0377-2217

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Abstract

While research in robust optimization has attracted considerable interest over the last decades, its algorithmic development has been hindered by several factors. One of them is a missing set of benchmark instances that make algorithm performance better comparable, and makes reproducing instances unnecessary. Such a benchmark set should contain hard instances in particular, but so far, the standard approach to produce instances has been to sample values randomly from a uniform distribution. In this paper we introduce a new method to produce hard instances for min-max combinatorial optimization problems, which is based on an optimization model itself. Our approach does not make any assumptions on the problem structure and can thus be applied to any combinatorial problem. Using the Selection and Traveling Salesman problems as examples, we show that it is possible to produce instances which are up to 500 times harder to solve for a mixed-integer programming solver than the current state-of-the-art instances.

Item Type: Journal Article
Journal or Publication Title: European Journal of Operational Research
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1800/1802
Subjects:
ID Code: 136271
Deposited By: ep_importer_pure
Deposited On: 17 Oct 2019 08:50
Refereed?: Yes
Published?: Published
Last Modified: 14 Feb 2020 05:54
URI: https://eprints.lancs.ac.uk/id/eprint/136271

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