带删失需求的单仓库多门店系统的资源自适应原始对偶学习

中文摘要 AI 辅助

单仓库多门店（OWMS）系统是一种基础库存网络，其中不可补货的仓库会随时间将共享库存分配给多个门店。现有OWMS学习策略基于针对初始平均资源率校准的固定目标构建，但这种固定目标架构在已实现的销售改变未来各期可用剩余资源后，无法重新调整中心。我们提出资源自适应原始对偶学习（Resource-Adaptive Primal-Dual Learning），这是一种新的学习框架，可在剩余资源状态演变时跟踪带删失需求的原始对偶求解路径。在每一期，当前资源率索引目标门店分配和对偶变量，而删失销售为更新两者提供梯度估计。该分析结合预期销售几何与移动目标论证，得到对数级预期悔值，优于现有OWMS学习策略的平方根阶保证。其底层设计与分析思路或可为其他存在消耗性共享资源的在线学习问题提供参考。数值实验进一步证明，该实用变体在不同时间跨度和库存机制下均具有良好的有限跨度性能。

英文摘要

The one-warehouse multi-store (OWMS) system is a fundamental inventory network in which a nonreplenishable warehouse allocates shared stock across multiple stores over time. Existing OWMS learning policies are built around a fixed target calibrated to the initial average resource rate, but such a fixed-target architecture cannot re-center after realized sales change the remaining resource available per future period. We develop Resource-Adaptive Primal-Dual Learning, a new learning framework that tracks the primal-dual resolving path with censored demand as the remaining-resource state evolves. In each period, the current resource rate indexes the target store allocations and dual variable, while censored sales provide gradient estimates for updating both. The analysis combines expected-sales geometry with a moving-target argument to yield logarithmic expected regret, improving on the state-of-the-art square-root-order guarantees of existing OWMS learning policies. The underlying design and analytical ideas may inform other online learning problems with depleting shared resources. Numerical experiments further demonstrate good finite-horizon performance of a practical variant across different horizon lengths and inventory regimes.

发表机构

• School of Management, Fudan University（复旦大学管理学院）

机构由 AI 辅助整理，请以论文原文为准。