跨境电商海外仓选址与库存配置的随机优化模拟研究

Stochastic Optimization Simulation for Overseas Warehouse Location Selection and Inventory Allocation in Cross-Border E-Commerce

摘要: 本研究针对跨境电商海外仓网络设计中面临的需求、物流及汇率多重不确定性问题，构建了一个集成蒙特卡洛模拟的随机优化模型。以欧洲主要市场为例，对比分析了集中式(仅波兰仓)、双仓式(波兰 + 德国)及多仓式等多种网络配置方案。通过模拟数万次随机场景，量化评估了各方案的期望总成本、风险成本(95%分位数)及服务水平。研究发现，双仓式方案在期望成本仅增加15%的情况下，能将极端风险降低30%以上，并保障95%以上的订单实现5日内送达，是当前市场环境下最佳的成本–风险–服务权衡点。本研究为企业提供了一种数据驱动的决策框架，证明忽略不确定性的“最优解”在现实中可能蕴含巨大风险。

Abstract: This study addresses the multi-source uncertainties—demand, logistics, and exchange rates—in the design of cross-border e-commerce overseas warehouse networks by constructing a stochastic optimization model integrated with Monte Carlo simulation. Taking major European markets as an example, the study compares various network configurations, including a centralized pattern (Poland-only warehouse), a dual-warehouse pattern (Poland + Germany), and multi-warehouse patterns. Through tens of thousands of random scenario simulations, the expected total cost, risk cost (95th percentile), and service level of each scheme are quantified. The findings indicate that the dual-warehouse scheme increases the expected cost by only about 15% while reducing extreme risk by more than 30% and ensuring that over 95% of orders are delivered within five days, representing the optimal trade-off among cost, risk, and service level in the current market environment. This research provides enterprises with a data-driven decision-making framework, demonstrating that the “optimal solution” derived by ignoring uncertainties may conceal substantial risks in practice.