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Communication Dans Un Congrès Année : 2022

A Reinforcement Learning Powered Digital Twin to Support Supply Chain Decisions

Guillaume Martin
Raphaël Oger

Résumé

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.
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Dates et versions

hal-04074719 , version 1 (19-04-2023)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Guillaume Martin, Raphaël Oger. A Reinforcement Learning Powered Digital Twin to Support Supply Chain Decisions. HICSS2022 - Hawaii International Conference on System Sciences, Jan 2022, Hawaii, United States. pp.2291-2299, ⟨10.24251/HICSS.2022.287⟩. ⟨hal-04074719⟩
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