论文标题
数据驱动的相关方案预测可靠的组合优化
Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization
论文作者
论文摘要
我们研究（两阶段）强大组合优化问题与离散不确定性的迭代方法。我们提出了一种基于机器的启发式启发式，以确定提供强大下限的启动场景。为此，我们设计与维度无关的特征，并在小维情况下训练随机的森林分类器。实验表明，我们的方法改善了比训练集中包含的更大实例的解决方案过程，还提供了特征的重要性得分，该得分可以洞悉场景属性的作用。
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.
