  A class of non-convex optimization problems with DC objective function is
studied, where DC stands for being representable as the difference $f=g-h$ of
two convex functions $g$ and $h$. In particular, we deal with the special case
where one of the two convex functions $g$ or $h$ is polyhedral. In case $g$ is
polyhedral, we show that a solution of the DC program can be obtained from a
solution of an associated polyhedral projection problem. In case $h$ is
polyhedral, we prove that a solution of the DC program can be obtained by
solving a polyhedral projection problem and finitely many convex programs.
Since polyhedral projection is equivalent to multiple objective linear
programming (MOLP), a MOLP solver (in the second case together with a convex
programming solver) can be used to solve instances of DC programs with
polyhedral component. Numerical examples are provided, among them an
application to locational analysis.
