  The repeated solution of large-scale optimization problems arises frequently
in process systems engineering tasks. Decomposition-based solution methods have
been widely used to reduce the corresponding computational time, yet their
implementation has multiple steps that are difficult to configure. We propose a
machine learning approach to learn the optimal initialization of such
algorithms which minimizes the computational time. Active and supervised
learning is used to learn a surrogate model that predicts the computational
performance for a given initialization. We apply this approach to the
initialization of Generalized Benders Decomposition for the solution of mixed
integer model predictive control problems. The surrogate models are used to
find the optimal number of initial cuts that should be added in the master
problem. The results show that the proposed approach can lead to a significant
reduction in solution time, and active learning can reduce the data required
for learning.
