发表机构
Amazon RME; IE University; UniPD（亚马逊RME; IE大学; 帕多瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对两级备件网络设计，提出结合图神经网络、变邻域搜索与集合划分的保守优化框架，在亚马逊案例中节省成本提升30.5%，并有效抑制代理模型乐观偏差。
AI 中文摘要
我们研究了两级备件库存网络的战略设计问题，其中评估每个候选拓扑结构都需要运行昂贵的库存优化模型。该设计将数百个站点划分为可行的集群，并为每个集群选择一个中央补货站点，以在维持服务水平的同时降低成本。由于优化器倾向于选择预测节省成本较高的候选方案，它可能会利用乐观的代理模型误差。我们开发了一个保守框架，结合了图神经网络集成、变邻域搜索和集合划分重组。代理模型基于精确的集群评估进行训练，而集成预测节省成本的下分位数则引导搜索以限制乐观偏差。搜索过程中发现的集群通过使用基于代理模型的目标系数的集合划分进行重组。最终网络使用精确库存模型进行评估，并且仅使用该评估结果来报告性能。在亚马逊北美网络中246个履约中心的案例研究中，该框架相比完全基于精确集群评估的优化基线，将综合节省成本提高了30.5%，同时在六次独立重复中保持了约99.8%的服务水平。在相同的计算预算下，图代理引导搜索在两种评分方案下均实现了比表格替代方案更高的平均精确节省成本。保守评分提高了两类代理模型的平均节省成本，并将图代理高估最终网络集群的比例从68%降至28%。在搜索生成的具有高代理评分候选方案中，预测和排序准确性有所下降，这表明随机留出集的性能可能无法完全表征优化过程中代理模型的质量。
英文摘要
We study the strategic design of a two-echelon spare-parts inventory network where evaluating each candidate topology requires an expensive inventory optimization model. The design partitions hundreds of sites into feasible clusters and selects a central replenishment site for each cluster to reduce costs while maintaining service levels. Because the optimizer favors candidates with high predicted savings, it can exploit optimistic surrogate errors. We develop a conservative framework combining a graph neural network ensemble, variable neighborhood search, and set-partitioning recombination. The surrogate is trained on exact cluster evaluations, while a lower quantile of ensemble-predicted savings guides the search to limit optimism. Clusters found during the search are recombined through set partitioning using surrogate-based objective coefficients. The resulting network is evaluated with the exact inventory model, and only this evaluation is used to report performance. In a case study of 246 fulfillment centers in Amazon's North American network, the framework improves combined savings by 30.5% over an optimization baseline based entirely on exact cluster evaluations, while maintaining approximately 99.8% service across six independent replications. Under equal computational budgets, graph-surrogate-guided search achieves higher mean exact savings than a tabular alternative under both scoring schemes. Conservative scoring improves mean savings for both surrogate classes and reduces the share of final-network clusters overestimated by the graph surrogate from 68% to 28%. Predictive and ranking accuracy deteriorate among search-generated candidates with high surrogate scores, indicating that random holdout performance can incompletely characterize surrogate quality during optimization.
