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Deep reinforcement learning for inventory control: A roadmap

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  • Boute, Robert N.
  • Gijsbrechts, Joren
  • van Jaarsveld, Willem
  • Vanvuchelen, Nathalie

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.

Suggested Citation

  • Boute, Robert N. & Gijsbrechts, Joren & van Jaarsveld, Willem & Vanvuchelen, Nathalie, 2022. "Deep reinforcement learning for inventory control: A roadmap," European Journal of Operational Research, Elsevier, vol. 298(2), pages 401-412.
    • Handle: RePEc:eee:ejores:v:298:y:2022:i:2:p:401-412
    DOI: 10.1016/j.ejor.2021.07.016
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    2. Goedhart, Joost & Haijema, René & Akkerman, Renzo, 2023. "Modelling the influence of returns for an omni-channel retailer," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1248-1263.

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