  We consider Benders decomposition for solving two-stage stochastic programs
with complete recourse based on finite samples of the uncertain parameters. We
define the Benders cuts binding at the final optimal solution or the ones
significantly improving bounds over iterations as valuable cuts. We propose a
learning-enhanced Benders decomposition (LearnBD) algorithm, which adds a cut
classification step in each iteration to selectively generate cuts that are
more likely to be valuable cuts. The LearnBD algorithm includes two phases: (i)
sampling cuts and collecting information from training problems and (ii)
solving testing problems with a support vector machine (SVM) cut classifier. We
run the LearnBD algorithm on instances of capacitated facility location and
multi-commodity network design under uncertain demand. Our results show that
SVM cut classifier works effectively for identifying valuable cuts, and the
LearnBD algorithm reduces the total solving time of all instances for different
problems with various sizes and complexities.
