TY - JOUR
T1 - Deep reinforcement learning for inventory control
T2 - A roadmap
AU - Boute, Robert N.
AU - Gijsbrechts, Joren
AU - van Jaarsveld, Willem
AU - Vanvuchelen, Nathalie
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2022/4/16
Y1 - 2022/4/16
N2 - 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.
AB - 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.
KW - Inventory management
KW - Machine learning
KW - Neural networks
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85111846139&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2021.07.016
DO - 10.1016/j.ejor.2021.07.016
M3 - Review article
AN - SCOPUS:85111846139
SN - 0377-2217
VL - 298
SP - 401
EP - 412
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 2
ER -