A Reinforcement Learning Powered Digital Twin to Support Supply Chain Decisions

dc.contributor.author Martin, Guillaume
dc.contributor.author Oger, Raphaël
dc.date.accessioned 2021-12-24T17:38:33Z
dc.date.available 2021-12-24T17:38:33Z
dc.date.issued 2022-01-04
dc.description.abstract The complexity of making supply chain planning decisions is growing along with the Volatility, Uncertainty, Complexity and Ambiguity of supply chain environments. As a consequence, the complexity of designing adequate decision support systems is also increasing. New approaches emerged for supporting decisions, and digital twins is one of those. Concurrently, the artificial intelligence field is growing, including approaches such as reinforcement learning. This paper explores the potential of creating digital twins with reinforcement learning capabilities. It first proposes a framework for unifying digital twins and reinforcement learning into a single approach. It then illustrates how this framework is put into practice for making supply and delivery decisions within a drug supply chain use case. Finally, the results of the experiment are compared with results given by traditional approaches, showing the applicability of the proposed framework.
dc.format.extent 9 pages
dc.identifier.doi 10.24251/HICSS.2022.287
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79620
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Digital and Hyperconnected Supply Chain Systems
dc.subject deep learning
dc.subject digital twin
dc.subject reinforcement learning
dc.subject supply chain management
dc.title A Reinforcement Learning Powered Digital Twin to Support Supply Chain Decisions
dc.type.dcmi text
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