Abstract
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. © 2010 by the American Society for Engineering Management.
Recommended Citation
Z. Sui et al., "A Reinforcement Learning Approach for Inventory Replenishment in Vendor-managed Inventory Systems with Consignment Inventory," EMJ - Engineering Management Journal, vol. 22, no. 4, pp. 44 - 53, Taylor and Francis Group; Taylor and Francis, Dec 2010.
The definitive version is available at https://doi.org/10.1080/10429247.2010.11431878
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Simulation; Supply Chains; Vendor-Managed Inventory
International Standard Serial Number (ISSN)
1042-9247
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Taylor and Francis Group; Taylor and Francis, All rights reserved.
Publication Date
01 Dec 2010
