The multicommodity capacitated fixed-charge network design problem has been extensively studied in the literature due to its wide range of applications. Despite the fact that many sophisticated solution methods exist today, finding high-quality solutions to large-scale instances remains challenging. In this paper, we explore how a data-driven approach can help improve upon the state of the art. By leveraging machine learning models, we attempt to reveal patterns hidden in the data that might be difficult to capture with traditional optimization methods. For scalability, we propose a prediction method where the machine learning model is called at the level of each arc of the graph. We take advantage of off-the-shelf models trained via supervised learning to predict near-optimal solutions. Our experimental results include an algorithm design analysis that compares various integration strategies of predictions within local search algorithms. We benchmark the ML-based approach with respect to the state-of-the-art heuristic for this problem. The findings indicate that our method can outperform the leading heuristic on sets of instances sampled from a uniform distribution.

中文翻译：

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结合监督学习和本地搜索解决多商品容量固定收费网络设计问题


由于其广泛的应用范围，多商品电容固定电荷网络设计问题已在文献中得到广泛研究。尽管目前存在许多复杂的解决方案方法，但为大规模实例找到高质量的解决方案仍然具有挑战性。在本文中，我们探讨了数据驱动的方法如何帮助改进最先进的技术。通过利用机器学习模型，我们试图揭示隐藏在数据中的模式，而这些模式可能难以用传统的优化方法捕获。为了实现可扩展性，我们提出了一种预测方法，其中机器学习模型在图形的每个弧线的级别调用。我们利用通过监督学习训练的现成模型来预测近乎最优的解决方案。我们的实验结果包括算法设计分析，该分析比较了本地搜索算法中预测的各种集成策略。我们将基于 ML 的方法与此问题的最新启发式方法进行基准测试。结果表明，我们的方法可以在从均匀分布中采样的实例集上胜过领先的启发式方法。