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Deep reinforcement learning for inventory control: a Roadmap
Boute, Robert Gijsbrechts, Joren van Jaarsveld, Willem Vanvuchelen, Nathalie
Boute, Robert
Gijsbrechts, Joren
van Jaarsveld, Willem
Vanvuchelen, Nathalie
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2022
Journal
European Journal of Operational Research
Book
Publication Volume
298
Publication Issue
2
Publication Begin page
401
Publication End page
412
Publication Number of pages
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Abstract
Deep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice, may hamper their application in practice. This paper describes the key design choices of DRL algorithms to facilitate their implementation in inventory control. We also shed light on possible future research avenues that may elevate the current state-of-the-art of DRL applications for inventory control and broaden their scope by leveraging and improving on the structural policy insights within inventory research. Our discussion and roadmap may also spur future research in other domains within operations management.
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Keywords
Inventory Management, Machine Learning, Reinforcement Learning, Neural Networks