  Mixed integer Model Predictive Control (MPC) problems arise in the operation
of systems where discrete and continuous decisions must be taken simultaneously
to compensate for disturbances. The efficient solution of mixed integer MPC
problems requires the computationally efficient and robust online solution of
mixed integer optimization problems, which are generally difficult to solve. In
this paper, we propose a machine learning-based branch and check Generalized
Benders Decomposition algorithm for the efficient solution of such problems. We
use machine learning to approximate the effect of the complicating variables on
the subproblem by approximating the Benders cuts without solving the
subproblem, therefore, alleviating the need to solve the subproblem multiple
times. The proposed approach is applied to a mixed integer economic MPC case
study on the operation of chemical processes. We show that the proposed
algorithm always finds feasible solutions to the optimization problem, given
that the mixed integer MPC problem is feasible, and leads to a significant
reduction in solution time (up to 97% or 50x) while incurring small error (in
the order of 1%) compared to the application of standard and accelerated
Generalized Benders Decomposition.
