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Generating hard instances for robust combinatorial optimization

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>1/01/2020
<mark>Journal</mark>European Journal of Operational Research
Issue number1
Volume280
Number of pages12
Pages (from-to)34-45
Publication StatusPublished
Early online date22/07/19
<mark>Original language</mark>English

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.