Download This PaperCan Deep Reinforcement Learning Improve Inventory Management? Performance on Dual Sourcing, Lost Sales and Multi-Echelon Problems
26 Pages Posted: 3 Jan 2019 Last revised: 30 Jul 2019
Date Written: July 29, 2019
Abstract
The popularity of reinforcement learning is growing but is it effective in operations? We provide proof of concept that deep reinforcement learning (DRL) can be applied as a general-purpose technology to three classic, yet intractable inventory replenishment problems. Step-by-step guidance on how to apply DRL to a real data set is proffered together with the code and a careful discussion of its performance, strengths and weaknesses.
Keywords: artificial intelligence, deep reinforcement learning, inventory control, dual sourcing, lost sales, multi-echelon
JEL Classification: M11
Suggested Citation: Suggested Citation
Gijsbrechts, Joren and Boute, Robert N. and Van Mieghem, Jan Albert and Zhang, Dennis, Can Deep Reinforcement Learning Improve Inventory Management? Performance on Dual Sourcing, Lost Sales and Multi-Echelon Problems (July 29, 2019). Available at SSRN: https://ssrn.com/abstract=3302881 or http://dx.doi.org/10.2139/ssrn.3302881
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