Scientists in Operations Research and Optimization face a problem which increases in it's difficulty almost daily. More and more different objective functions have been considered for minimization, and it is therefore more and more difficult to find a new, previously unconsidered one. We propose a simple remedy for this situation.
While it might be possible to come up with new research results even for objective functions which have been considered before, this is seldom seen as a solution to the problem described above. In order to come up with results that can be classified as new, it is often the case that one has to know at least a part of the literature. As a consequence, the generation of new problems can be considered as easier. This the approach taken in this paper.
We propose a simple problem generator that can automatically generate new optimization problems. These problems can then be analyzed by scientists with, e. g., standard techniques, and standard algorithms can be proposed to solve them.