Can Deep Reinforcement Learning Improve Inventory Management? Performance on Dual Sourcing, Lost Sales and Multi-Echelon Problems
33 Pages Posted: 3 Jan 2019 Last revised: 8 Oct 2020
Date Written: October 6, 2020
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