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 ...
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.