We study iterative methods for (two-stage) robust combinatorial optimization problems
with discrete uncertainty. We propose a machine-learning-based heuristic to determine
starting scenarios that provide strong lower bounds. To this end, we design dimension-independent
features and train a Random Forest Classifier on small-dimensional instances. Experiments
show that our method improves the solution process for larger instances than contained
in the training set and also provides a feature importance-score which gives insights
into the role of scenario properties.
View Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization