Abstract
Nonconvex generalized disjunctive programs (GDPs) frequently arise in chemical engineering applications and are commonly reformulated as mixed-integer nonlinear programs (MINLPs). However, nonconvexities in these reformulations often lead to numerical difficulties, sensitivity to initialization, and degraded solution quality when solved with general-purpose MINLP solvers. This work proposes a two-phase strategy to mitigate these effects by generating improved initial points through the solution of a sequence of relaxed MINLPs, which are subsequently used to initialize the original formulation. The approach is evaluated on a family of purely disjunctive benchmark problems, referred to as the Crescent problems, with sizes ranging from 60 to 1000 binary variables. Numerical experiments using the DICOPT and SBB solvers assess performance in terms of objective value distributions, the percentage of feasible initial points, and average constraint violation. The results indicate that the proposed strategy improves solution quality, increases the likelihood of feasibility, and reduces the magnitude of constraint violations across all problem sizes.