Benders Cut Classification via Support Vector Machines for Solving Two-Stage Stochastic Programs

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