Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms.
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| Title: | Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms. |
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| Authors: | Koulouriotis, D.E.1 (AUTHOR) jimk@pme.duth.gr, Xanthopoulos, A.S.1 (AUTHOR), Tourassis, V.D.1 (AUTHOR) |
| Source: | International Journal of Production Research. May2010, Vol. 48 Issue 10, p2887-2912. 26p. 8 Diagrams, 10 Charts, 4 Graphs. |
| Subjects: | Assembly line methods, Advanced planning & optimization, Manufacturing process automation, Genetic algorithms, Decision support systems, Time series analysis, Statistical process control |
| Abstract: | Several efficient pull production control policies for serial lines implementing the lean/JIT manufacturing philosophy can be found in the production management literature. A recent development that is less well-studied than the serial line case is the application of pull-type policies to assembly systems where manufacturing operations take place both sequentially and in parallel. Systems of this type contain assembly stations where two or more parts from lower hierarchical manufacturing stations merge in order to produce a single part of the subsequent stage. In this paper we extend the application of the Base Stock, Kanban, CONWIP, CONWIP/Kanban Hybrid and Extended Kanban production control policies to assembly systems that produce final products of a single type. Discrete-event simulation is utilised in order to evaluate the performance of serial lines and assembly systems. It is essential to determine the best control parameters for each policy when operating in the same environment. The approach that we propose and probe for the problem of control parameter selection is that of a genetic algorithm with resampling, a technique used for the optimisation of stochastic objective functions. Finally, we report our findings from numerical experiments conducted for two serial line simulation scenarios and two assembly system simulation scenarios. [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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 49143964 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Koulouriotis%2C+D%2EE%2E%22">Koulouriotis, D.E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jimk@pme.duth.gr</i><br /><searchLink fieldCode="AR" term="%22Xanthopoulos%2C+A%2ES%2E%22">Xanthopoulos, A.S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tourassis%2C+V%2ED%2E%22">Tourassis, V.D.</searchLink><relatesTo>1</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>. May2010, Vol. 48 Issue 10, p2887-2912. 26p. 8 Diagrams, 10 Charts, 4 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Assembly+line+methods%22">Assembly line methods</searchLink><br /><searchLink fieldCode="DE" term="%22Advanced+planning+%26+optimization%22">Advanced planning & optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+process+automation%22">Manufacturing process automation</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+process+control%22">Statistical process control</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Several efficient pull production control policies for serial lines implementing the lean/JIT manufacturing philosophy can be found in the production management literature. A recent development that is less well-studied than the serial line case is the application of pull-type policies to assembly systems where manufacturing operations take place both sequentially and in parallel. Systems of this type contain assembly stations where two or more parts from lower hierarchical manufacturing stations merge in order to produce a single part of the subsequent stage. In this paper we extend the application of the Base Stock, Kanban, CONWIP, CONWIP/Kanban Hybrid and Extended Kanban production control policies to assembly systems that produce final products of a single type. Discrete-event simulation is utilised in order to evaluate the performance of serial lines and assembly systems. It is essential to determine the best control parameters for each policy when operating in the same environment. The approach that we propose and probe for the problem of control parameter selection is that of a genetic algorithm with resampling, a technique used for the optimisation of stochastic objective functions. Finally, we report our findings from numerical experiments conducted for two serial line simulation scenarios and two assembly system simulation scenarios. [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: 10.1080/00207540802603759 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 2887 Subjects: – SubjectFull: Assembly line methods Type: general – SubjectFull: Advanced planning & optimization Type: general – SubjectFull: Manufacturing process automation Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Decision support systems Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Statistical process control Type: general Titles: – TitleFull: Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Koulouriotis, D.E. – PersonEntity: Name: NameFull: Xanthopoulos, A.S. – PersonEntity: Name: NameFull: Tourassis, V.D. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 48 – Type: issue Value: 10 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
