Generating hard instances for robust combinatorial optimization

Marc Goerigk and Stephen J. Maher

European Journal of Operational Research, 2020, vol. 280, issue 1, 34-45

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.

Keywords: Robustness and sensitivity analysis; Robust optimization; Problem benchmarking; Problem generation; Combinatorial optimization (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:280:y:2020:i:1:p:34-45

DOI: 10.1016/j.ejor.2019.07.036

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