Paper Announcement: A Practical Approach to Replenishment Optimization with Extended (R, s, Q) Policy and Probabilistic Models

Paper Announcement: A Practical Approach to Replenishment Optimization with Extended (R, s, Q) Policy and Probabilistic Models

摘要

电商库存管理面临"多则积压，少则断货"的悖论。Zalando研发的智能补货引擎通过概率预测+离散事件模拟破解难题：1）用LightGBM生成需求概率分布，捕捉极端波动；2）扩展(R,s,Q)策略，引入首发量Q₀和生命周期截止点t_limit；3）蒙特卡洛模拟评估数千种策略。实测显示GMV提升22%，缺货率降低34%，70%以上商户受益。这套系统证明：拥抱不确定性才是最优解。