A supervised machine learning approach for the optimisation of the assembly line feeding mode selection.
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| Title: | A supervised machine learning approach for the optimisation of the assembly line feeding mode selection. |
|---|---|
| Authors: | Zangaro, Francesco1,2 (AUTHOR) francesco.zangaro@phd.unipd.it, Minner, Stefan1 (AUTHOR), Battini, Daria2 (AUTHOR) |
| Source: | International Journal of Production Research. Aug2021, Vol. 59 Issue 16, p4881-4902. 22p. 4 Diagrams, 9 Charts, 4 Graphs. |
| Subjects: | Supervised learning, Assembly line methods, Decision trees, Problem solving, Timberline |
| Abstract: | The Line Feeding Problem (LFP) involves the delivery of components to the production area. Previous models minimise the delivery costs and optimally assign each component to a line feeding mode between line stocking, kitting, and sequencing but cannot provide easily comprehensible guidelines. We use the Classification And Regression Tree (CART) algorithm to develop, in a supervised way, a decision tree based on problems that are solved with a Mixed Integer Programming (MIP) model for training purposes. Based on selected attributes of the components and the manufacturing environment, the decision tree suggests a line feeding mode for every component. For a synthetically determined training and evaluation data set, we find that the classification tree can predict the line feeding mode with an average classification accuracy of 78.49%. After the decision tree is implemented and a line feeding mode is selected for each component, an infeasible solution might occur. We develop a repair approach that solves this problem with an average cost deviation from the optimal solution of 0.38%. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 151932913 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A supervised machine learning approach for the optimisation of the assembly line feeding mode selection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zangaro%2C+Francesco%22">Zangaro, Francesco</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> francesco.zangaro@phd.unipd.it</i><br /><searchLink fieldCode="AR" term="%22Minner%2C+Stefan%22">Minner, Stefan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Battini%2C+Daria%22">Battini, Daria</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Aug2021, Vol. 59 Issue 16, p4881-4902. 22p. 4 Diagrams, 9 Charts, 4 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Assembly+line+methods%22">Assembly line methods</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Timberline%22">Timberline</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Line Feeding Problem (LFP) involves the delivery of components to the production area. Previous models minimise the delivery costs and optimally assign each component to a line feeding mode between line stocking, kitting, and sequencing but cannot provide easily comprehensible guidelines. We use the Classification And Regression Tree (CART) algorithm to develop, in a supervised way, a decision tree based on problems that are solved with a Mixed Integer Programming (MIP) model for training purposes. Based on selected attributes of the components and the manufacturing environment, the decision tree suggests a line feeding mode for every component. For a synthetically determined training and evaluation data set, we find that the classification tree can predict the line feeding mode with an average classification accuracy of 78.49%. After the decision tree is implemented and a line feeding mode is selected for each component, an infeasible solution might occur. We develop a repair approach that solves this problem with an average cost deviation from the optimal solution of 0.38%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2020.1851793 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 4881 Subjects: – SubjectFull: Supervised learning Type: general – SubjectFull: Assembly line methods Type: general – SubjectFull: Decision trees Type: general – SubjectFull: Problem solving Type: general – SubjectFull: Timberline Type: general Titles: – TitleFull: A supervised machine learning approach for the optimisation of the assembly line feeding mode selection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zangaro, Francesco – PersonEntity: Name: NameFull: Minner, Stefan – PersonEntity: Name: NameFull: Battini, Daria IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 59 – Type: issue Value: 16 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
