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arXiv · 2203.16642

Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization

Marc Goerigk·Jannis Kurtz
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Abstract

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.

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Marc Goerigk, Jannis Kurtz. 2022-03-30. Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization. https://arxiv.org/abs/2203.16642

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