Can 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
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
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