跨境履约中心选择：结合双重稳健估计与线性规划的多阶段优化模型
Cross-Border Fulfillment Center Selection via Doubly Robust Estimation and Linear Programming
AI 导读
一项研究提出跨境履约中心选择模型（XB FC Selection Model），结合预测、双重稳健估计与线性规划，在固定国内网络下选择最优来源履约中心。基于某大型电商公司 12 个月出货数据、三条国际运输走廊评估，该模型相比人工专家策略将配送时间降低 8.5-51.1%，物流成本降低 4.4-48.3%，5/5 目标合规率最高提升 6.5 个百分点，结果均具统计显著性。
正文 · 原文
Abstract:We introduce the cross-border (XB) fulfillment center (FC) Selection Model: a multi-stage optimization framework combining forecasting, doubly robust estimation, and linear programming to select optimal source FCs. For each source-destination shipping arc, the objective is to choose a subset of source-country FCs that minimize delivery time and cost while maximizing profit and shipped units under a fixed domestic network. We evaluate the model on three production-scale international shipping corridors for a large e-commerce company using 12 months of shipment data, employing both aggregate offline replay and shipment-level counterfactual nearest-neighbor matching. Across all shipping corridors, the XB FC Selection Model achieves 8.5-51.1% reductions in delivery time and 4.4-48.3% reductions in logistics cost relative to human expert-defined FC selection policies, with up to 6.5 percentage-point improvements in 5/5 target compliance (5 days, $5 USD). Pareto analysis shows the model consistently dominates single-objective ranking strategies and unconstrained LP on the efficiency-reallocation frontier, with all results statistically significant.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.14174 [cs.AI] |
| (or arXiv:2611.14174v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14174 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kriti Mahajan [view email]
[v1]
Tue, 10 Nov 2026 15:01:03 UTC (3,945 KB)
来源：arXiv cs.AI · arxiv.org