A Reinforcement Learning Approach for Inventory Replenishment in Vendor-Managed Inventory Systems With Consignment Inventory

被引:28
|
作者
Sui, Zheng
Gosavi, Abhijit [1 ]
Lin, Li [2 ]
机构
[1] Missouri Univ Sci & Technol, Rolla, MO 65409 USA
[2] SUNY Buffalo, Buffalo, NY 14260 USA
关键词
Vendor-Managed Inventory; Supply Chains; Simulation; STOCK REPLENISHMENT; ALLOCATION; POLICY; MODEL;
D O I
10.1080/10429247.2010.11431878
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In a Vendor-Managed Inventory (VMI) system, the supplier makes decisions of inventory management for the retailer; the retailer is not responsible for placing orders. There is a dearth of optimization models for replenishment strategies for VMI systems, and the industry relies on well-understood, but simple models, e.g., the newsvendor rule. In this article, we propose a methodology based on reinforcement learning, which is rooted in the Bellman equation, to determine a replenishment policy in a VMI system with consignment inventory. We also propose rules based on the newsvendor rule. Our numerical results show that our approach can outperform the newsvendor.
引用
收藏
页码:44 / 53
页数:10
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