Position Paper: Post-Solve Robustness in Decision Engines: Feasible Regions and Smoothness Under Perturbations

This paper introduces a missing layer in optimization pipelines to address the post-solve robustness gap in Mixed-Integer Linear Programming (MILP) decision engines. It formalizes an epsilon-near-optimal feasible neighborhood and solution smoothness to assess how far a solved incumbent can be trusted under parameter perturbations.