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Article: Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities

TitleReinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
Authors
YAN, YChow, AHFHo, CPKuo, YHWU, QYing, C
Issue Date2022
Citation
Transportation Research Part E: Logistics and Transportation Review, 2022, v. 162, p. 102712 How to Cite?
DOI: http://dx.doi.org/10.1016/j.tre.2022.102712
Persistent Identifierhttp://hdl.handle.net/10722/317354

 

DC FieldValueLanguage
dc.contributor.authorYAN, Y-
dc.contributor.authorChow, AHF-
dc.contributor.authorHo, CP-
dc.contributor.authorKuo, YH-
dc.contributor.authorWU, Q-
dc.contributor.authorYing, C-
dc.date.accessioned2022-10-07T10:19:00Z-
dc.date.available2022-10-07T10:19:00Z-
dc.date.issued2022-
dc.identifier.citationTransportation Research Part E: Logistics and Transportation Review, 2022, v. 162, p. 102712-
dc.identifier.urihttp://hdl.handle.net/10722/317354-
dc.languageeng-
dc.relation.ispartofTransportation Research Part E: Logistics and Transportation Review-
dc.titleReinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities-
dc.typeArticle-
dc.identifier.emailKuo, YH: yhkuo@hku.hk-
dc.identifier.authorityKuo, YH=rp02314-
dc.identifier.doi10.1016/j.tre.2022.102712-
dc.identifier.hkuros337565-
dc.identifier.volume162-
dc.identifier.spage102712-
dc.identifier.epage102712-

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