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Article: A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0

TitleA GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0
Authors
Lee, KHC
Keywordsanticipatory shipping
association rule mining
data mining
genetic algorithms
retail supply chain
Issue Date2017
Citation
International Journal of Production Research, 2017, v. 55, p. 593-605 How to Cite?
DOI: http://dx.doi.org/10.1080/00207543.2016.1221162
Persistent Identifierhttp://hdl.handle.net/10722/239552
ISSN
0020-7543
2023 Impact Factor: 7.0
2023 SCImago Journal Rankings: 2.668
ISI Accession Number ID
WOS:000390417200017

 

DC FieldValueLanguage
dc.contributor.authorLee, KHC-
dc.date.accessioned2017-03-21T09:15:43Z-
dc.date.available2017-03-21T09:15:43Z-
dc.date.issued2017-
dc.identifier.citationInternational Journal of Production Research, 2017, v. 55, p. 593-605-
dc.identifier.issn0020-7543-
dc.identifier.urihttp://hdl.handle.net/10722/239552-
dc.languageeng-
dc.relation.ispartofInternational Journal of Production Research-
dc.subjectanticipatory shipping-
dc.subjectassociation rule mining-
dc.subjectdata mining-
dc.subjectgenetic algorithms-
dc.subjectretail supply chain-
dc.titleA GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0-
dc.typeArticle-
dc.identifier.emailLee, KHC: leeckh@hku.hk-
dc.identifier.doi10.1080/00207543.2016.1221162-
dc.identifier.scopuseid_2-s2.0-84981234426-
dc.identifier.hkuros271528-
dc.identifier.volume55-
dc.identifier.spage593-
dc.identifier.epage605-
dc.identifier.eissn1366-588X-
dc.identifier.isiWOS:000390417200017-
dc.identifier.issnl0020-7543-

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