Deep Reinforcement Learning for Supply Chain Synchronization
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Date
2022-01-04
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Supply chain synchronization can prevent the “bullwhip effect” and significantly mitigate ripple effects caused by operational failures. This paper demonstrates how deep reinforcement learning agents based on the proximal policy optimization algorithm can synchronize inbound and outbound flows if end-toend visibility is provided. The paper concludes that the proposed solution has the potential to perform adaptive control in complex supply chains. Furthermore, the proposed approach is general, task unspecific, and adaptive in the sense that prior knowledge about the system is not required.
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Simulation Modeling and Digital Twins for Decision Making in the Age of Industry 4.0, deep rl, ppo, reinforcement learning, supply chain
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9 pages
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Proceedings of the 55th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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