A parametric mixed-integer global optimization framework for the solution of process engineering problems under uncertainty

This paper presents two algorithms for the global solution of parametric mixed-integer nonlinear programming problems. The basic idea of both the algorithms is to create parametric convex underestimators and overestimators of the nonconvex functions, which converge to the global solution by using branch and bound techniques on the space of continuous variables. However, the proposed algorithms differ from each other in the way the integer solutions are obtained. While the first algorithm is based upon a branch and bound framework, the second algorithm relies on introducing cuts.