  Global optimization problems with a quasi-concave objective function and
linear constraints are studied. We point out that various other classes of
global optimization problems can be expressed in this way. We present two
algorithms, which can be seen as slight modifications of Benson-type algorithms
for multiple objective linear programs (MOLP). The modification of the MOLP
algorithms results in a more efficient treatment of the studied optimization
problems. This paper generalizes results of Schulz and Mittal on quasi-concave
problems and Shao and Ehrgott on multiplicative linear programs. Furthermore,
it improves results of L\"ohne and Wagner on minimizing the difference $f=g-h$
of two convex functions $g$, $h$ where either $g$ or $h$ is polyhedral.
Numerical examples are given and the results are compared with the global
optimization software BARON.
