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.

翻译：我们研究具有离散不确定性的(两阶段)稳健组合优化问题的迭接方法。我们建议采用基于机器学习的超常法来确定提供下限强的起始情景。为此,我们设计了维度独立的特征,并培训了小型实例的随机森林分类器。实验表明,我们的方法改进了比培训成套方法中包含的更大案例的解决方案进程,并提供了一个特别重要分级,揭示了情景属性的作用。