结合监督学习与局部搜索求解多商品容量受限固定费用网络设计问题
Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem
AI总结：
针对多商品容量受限固定费用网络设计问题，提出在弧层面用监督学习预测近优解并集成到局部搜索中，实验显示其在均匀分布实例上优于领先启发式算法。
摘要：
多商品容量受限固定费用网络设计问题因其广泛的应用而在文献中得到了大量研究。尽管如今已存在许多复杂的求解方法，但为大规模实例寻找高质量解仍然具有挑战性。本文探讨数据驱动方法如何帮助改进当前最先进水平。通过利用机器学习模型，我们试图揭示隐藏在数据中、传统优化方法可能难以捕捉的模式。为实现可扩展性，我们提出一种预测方法，在图的每条弧层面调用机器学习模型。我们利用通过监督学习训练的现成模型来预测近优解。实验结果包括一项算法设计分析，比较了局部搜索算法中预测的各种集成策略。我们针对该问题的最先进启发式算法对基于机器学习的方法进行基准测试。研究结果表明，在从均匀分布采样的实例集合上，我们的方法能够优于领先的启发式算法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
