Use of proximal policy optimization for the joint replenishment problem
Publication typeJournal article with impact factor
JournalComputers in Industry
MetadataShow full item record
AbstractDeep 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.
KeywordCollaborative Shipping, Physical Internet, Joint Replenishment Problem, Machine Learning, Deep Reinforcement Learning, Proximal Policy Optimization
Knowledge Domain/IndustryOperations & Supply Chain Management
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