  In this paper, we describe a comprehensive algorithmic framework for solving
mixed integer bilevel linear optimization problems (MIBLPs) using a generalized
branch-and-cut approach. The framework presented merges features from existing
algorithms (for both traditional mixed integer linear optimization and MIBLPs)
with new techniques to produce a flexible and robust framework capable of
solving a wide range of bilevel optimization problems. The framework has been
fully implemented in the open-source solver MibS. The paper describes the
algorithmic options offered by MibS and presents computational results
evaluating the effectiveness of the various options for the solution of a
number of classes of bilevel optimization problems from the literature.
