Mixed-Integer MultiParametric Approach based on Machine Learning Techniques

This paper investigates the extension of a MultiParametric approach based on surrogate models (Meta-MultiParametric approach, M-MP) in order to handle general Mixed- Integer (MI) optimization problems involving Uncertain Parameters (UPs). The method harnesses metamodeling and clustering techniques in order to approximate black box relations between the optimal values of the continuous variables and the UPs, while Classification Techniques (CT) are employed to identify the optimal values of the integer variables also as a function of the UPs. The results of applying the method to a benchmark case-study show a high prediction accuracy of the optimal solutions, saving computational effort and overpassing the complex mathematical procedures required by the standard MultiParametric Programming methods.