Deep reinforcement learning for inventory control: A roadmap

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

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)401-412
Number of pages12
JournalEuropean Journal of Operational Research
Volume298
Issue number2
DOIs
StatePublished - Apr 16 2022
Externally publishedYes

Keywords

  • Inventory management
  • Machine learning
  • Neural networks
  • Reinforcement learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Fingerprint

Dive into the research topics of 'Deep reinforcement learning for inventory control: A roadmap'. Together they form a unique fingerprint.

Cite this