Enhancing Inbound Logistics Performance: Machine Learning-Based Prediction of Port-to-Port Lead Time and Target Setting

Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/168558

Full metadata record

DC Field	Value	Language
dc.creator	Samuel José Amorim Rocha
dc.date.accessioned	2026-01-30T00:56:24Z	-
dc.date.available	2026-01-30T00:56:24Z	-
dc.date.issued	2025-07-21
dc.date.submitted	2025-08-09
dc.identifier.other	sigarra:734574
dc.identifier.uri	https://hdl.handle.net/10216/168558	-
dc.language.iso	eng
dc.rights	embargoedAccess
dc.subject	Ciências exactas e naturais
dc.subject	Natural sciences
dc.title	Enhancing Inbound Logistics Performance: Machine Learning-Based Prediction of Port-to-Port Lead Time and Target Setting
dc.type	Relatório de Estágio
dc.date.embargo	2035-07-20
dc.contributor.uporto	Faculdade de Ciências
dc.identifier.tid	204168910
dc.subject.fos	Ciências exactas e naturais
dc.subject.fos	Natural sciences
thesis.degree.discipline	Mestrado em Engenharia Física
thesis.degree.grantor	Faculdade de Ciências
thesis.degree.grantor	Universidade do Porto
thesis.degree.level	1
rcaap.embargofct	A confidentiality period is requested to ensure that sensitive business information remains protected and that any eventual publication or disclosure complies with the company's internal approval processes and confidentiality agreements.
Appears in Collections:	FCUP - Relatório de Estágio

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734574.pdf Restricted Access	Enhancing Inbound Logistics Performance: Machine Learning- Based Prediction of Port-to-Port Lead Time and Target Setting	2.29 MB	Adobe PDF	View/Open