Use of proximal policy optimization for the joint replenishment problem
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BouteR_CII_Use of Proximal Policy ...
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2022-07-19
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Post-print
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Journal article with impact factorPublication Year
2020Journal
Computers in IndustryPublication Volume
119Publication Issue
August
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Deep reinforcement learning has been coined as a promising research avenue to solve sequential decision making problems, especially if few is known about the optimal policy structure. We apply the proximal policy optimization algorithm to the intractable joint replenishment problem. We demonstrate how the algorithm approaches the optimal policy structure and outperforms two other heuristics. Its deployment in supply chain control towers can orchestrate and facilitate collaborative shipping in the Physical Internet.Keyword
Collaborative Shipping, Physical Internet, Joint Replenishment Problem, Machine Learning, Deep Reinforcement Learning, Proximal Policy OptimizationKnowledge Domain/Industry
Operations & Supply Chain Managementae974a485f413a2113503eed53cd6c53
10.1016/j.compind.2020.103239
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