Enhancing Supply Chain Resilience Through Metaheuristic-Optimized Predictive Analytics: An Interpretable XGB Framework for Late-Delivery Risk Prediction.
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| Title: | Enhancing Supply Chain Resilience Through Metaheuristic-Optimized Predictive Analytics: An Interpretable XGB Framework for Late-Delivery Risk Prediction. |
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| Authors: | Zidan, Saied1, 230635025@std.akun.edu.tr, Adegboye, Oluwatayomi Rereloluwa1, Alzubi, Ahmad Bassam1 |
| Source: | Applied Sciences (2076-3417); May2026, Vol. 16 Issue 10, p5013, 27p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 194142407 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/app16105013 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 5013 Titles: – TitleFull: Enhancing Supply Chain Resilience Through Metaheuristic-Optimized Predictive Analytics: An Interpretable XGB Framework for Late-Delivery Risk Prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zidan, Saied – PersonEntity: Name: NameFull: Adegboye, Oluwatayomi Rereloluwa – PersonEntity: Name: NameFull: Alzubi, Ahmad Bassam IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20763417 Numbering: – Type: volume Value: 16 – Type: issue Value: 10 Titles: – TitleFull: Applied Sciences (2076-3417) Type: main |
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