使用扩展 (R,s,Q) 策略和概率模型进行补充优化的实用方法
Alva Presbitero1, Andreas Syrén2, Hagop Dippel2
1Zalando, 10243, Berlin, Germany. alva.presbitero@zalando.de.
Scientific reports
|December 19, 2025
概括
这项研究将概率需求预测与电子商务库存政策优化相结合. 新的Zalando电子商务操作系统 (ZEOS) 工具在动态的零售环境中提高了效率和利能力.
科学领域:
- 运营研究 运营研究
- 电子商务物流电子商务物流
- 供应链管理 供应链管理
背景情况:
- 由于需求波动和复杂的履行网络,有效的库存管理在电子商务中至关重要.
- 现有的优化模型经常使用简化的需求假设,无法捕捉现实世界的不确定性.
- 将预测建模与实际补给决策相结合是一个关键的挑战.
研究的目的:
- 将概率性需求预测与先进的库存政策优化整合起来.
- 扩大传统的[公式:参见文本]政策,用于分布式履行和季节性分类.
- 开发一个实用的工具,用于电子商务库存优化.
主要方法:
- 开发了Zalando电子商务操作系统 (ZEOS) 库存优化工具.
- 统一的一次性库存政策优化与概率梯度增强模型 (LightGBM).
- 调整了分布式网络和季节性产品组合的[公式:参见文本]政策.
主要成果:
- 与人类和经典基线相比,在完成成本后实现了商品总价值 (GMV) 和GMV的显著提升.
- 保持高运行可用性 ([公式:见文本]) 和需求填充率 ([公式:见文本]).
- 具有百分点目标和12周时间的概率预测显示出最佳表现.
结论:
- 该ZEOS工具有效地弥合了概率预测和电子商务政策优化.
- 这种方法提高了效率,降低了成本,提高了动态零售的利能力.
- 这项研究为复杂的库存管理挑战提供了开创性的解决方案.
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