Can Deep Reinforcement Learning Improve Inventory Management? Performance and Implementation of Dual Sourcing-Mode 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 to classic, yet intractable dual-sourcing or dual-mode 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, reinforcement learning, deep learning, dual sourcing inventory model

JEL Classification: M11