arXiv cs.AI· Kriti Mahajan, Patrick Bloebaum·· 10 小时前AI 评分26
跨境履约中心选择：基于双稳健估计与线性规划的优化模型
Cross-Border Fulfillment Center Selection via Doubly Robust Estimation and Linear Programming
AI 导读
研究者提出跨境履约中心（XB FC）选择模型，将预测、双稳健估计与线性规划结合，为每条发货弧选择最优来源国履约中心，在固定国内网络下同时优化配送时效、成本、利润与发货量。
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