Can Deep Reinforcement Learning Improve Inventory Management? Performance on Dual Sourcing, Lost Sales and Multi-Echelon Problems

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