发表机构
Northeastern University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究电子商务中预订单运输成本估计问题，提出受生产启发的多阶段框架RouteCost，将其分解为多步骤，通过路线加权期望公式汇总成本估计，在大量订单等数据上提高了预测质量和校准，保留了路线级可解释性。
AI 中文摘要
在电子商务中，准确的预订单运输成本估计很重要，因为它会影响价格展示、利润规划和转化率。实际中，运输成本不仅受距离影响，还受目的地需求组合、计费重量、尺寸定价、附加费触发因素以及诸如货物合并等潜在运营影响。静态查找方法会遗漏重要的变化来源，而整体回归器可能利用强但非因果的相关性。我们提出了RouteCost，这是一个受生产启发的多阶段框架，将问题分解为时间感知需求预测、费用卡告知的基线定价、第二阶段残差校正和基于代理的箱式合并推理。通过路线加权期望公式汇总路线级成本估计，以生成产品级运输成本预测。在超过250,000个订单、260种产品和18个月的订单历史数据上，该框架提高了预测质量和总体校准，同时保留了路线级的可解释性。
英文摘要
Accurate pre-order shipping cost estimation is important in e-commerce because it affects price presentation, margin planning, and conversion. In practice, shipping cost is shaped not only by distance but also by destination demand mix, billable weight, dimensional pricing, surcharge triggers, and latent operational effects such as shipment consolidation. Static lookup methods therefore miss important sources of variation, while monolithic regressors may exploit strong but non-causal correlations. We propose RouteCost, a production-inspired multi-stage framework that decomposes the problem into time-aware demand forecasting, fee-card-informed baseline pricing, Stage 2 residual correction, and proxy-based box-consolidation inference. Route-level cost estimates are aggregated through a route-weighted expectation formulation to produce product-level shipping cost predictions. Across over 250,000 orders, 260 products, and 18 months of order history, the framework improves predictive quality and aggregate calibration while preserving route-level interpretability.
