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dc.contributor.authorVanvuchelen, Nathalie
dc.contributor.authorGijsbrechts, Joren
dc.contributor.authorBoute, Robert
dc.date.accessioned2020-04-06T20:04:29Z
dc.date.available2020-04-06T20:04:29Z
dc.date.issued2020en_US
dc.identifier.issn0166-3615
dc.identifier.doi10.1016/j.compind.2020.103239
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6464
dc.description.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.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCollaborative Shippingen_US
dc.subjectPhysical Interneten_US
dc.subjectJoint Replenishment Problemen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Reinforcement Learningen_US
dc.subjectProximal Policy Optimizationen_US
dc.titleUse of proximal policy optimization for the joint replenishment problemen_US
refterms.dateFOA2020-07-19T12:07:07Z
dc.identifier.journalComputers in Industryen_US
dc.source.volume119
dc.source.issueAugust
dc.contributor.departmentFaculty of Economics and Business, KU Leuven, Belgiumen_US
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleJournal article with impact factoren_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid102358en_US


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