Deep Reinforcement Learning for Inventory Control: A Roadmap

28 Pages Posted: 8 Jun 2021 Last revised: 6 Jul 2021

See all articles by Robert N. Boute

Robert N. Boute

KU Leuven - Faculty of Business and Economics (FEB); Vlerick Business School - Operations & Technology Management Center

Joren Gijsbrechts

Catholic University of Portugal (UCP) - Catolica Lisbon School of Business and Economics

Willem van Jaarsveld

Eindhoven University of Technology (TUE)

Nathalie Vanvuchelen

KU Leuven

Date Written: June 7, 2021

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.

Keywords: inventory management, machine learning, reinforcement learning, neural networks

Suggested Citation

Boute, Robert N. and Gijsbrechts, Joren and van Jaarsveld, Willem and Vanvuchelen, Nathalie, Deep Reinforcement Learning for Inventory Control: A Roadmap (June 7, 2021). Available at SSRN: https://ssrn.com/abstract=3861821 or http://dx.doi.org/10.2139/ssrn.3861821

Robert N. Boute

KU Leuven - Faculty of Business and Economics (FEB) ( email )

Naamsestraat 69
Leuven, B-3000
Belgium

Vlerick Business School - Operations & Technology Management Center ( email )

Belgium

Joren Gijsbrechts (Contact Author)

Catholic University of Portugal (UCP) - Catolica Lisbon School of Business and Economics ( email )

Palma de Cima
Lisbon, 1649-023
Portugal

Willem van Jaarsveld

Eindhoven University of Technology (TUE) ( email )

PO Box 513
Eindhoven, 5600 MB
Netherlands

Nathalie Vanvuchelen

KU Leuven ( email )

Oude Markt 13
Leuven, Vlaams-Brabant 3000
Belgium

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