CELL FORMATION DESIGN USING FUZZY RELATIONS BASED ON A SIMILARITY COEFFICIENT.
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| Title: | CELL FORMATION DESIGN USING FUZZY RELATIONS BASED ON A SIMILARITY COEFFICIENT. |
|---|---|
| Authors: | G., Naadimuthu1 naadi@fdu.edu, P., Gultom2 gultom@ksu.edu, S., Lee E.2 lee@ksu.edu |
| Source: | Advances in Production Engineering & Management. Jun2011, Vol. 6 Issue 2, p77-86. 10p. 4 Diagrams, 3 Charts. |
| Subjects: | Fuzzy relational calculus, Coefficient of concordance, Manufacturing cells, Matrix analytic methods, Matrix derivatives |
| Abstract: | Based on literature survey results, the cell formation design is one of the most important operations in cellular manufacturing systems (CMS), since it impacts various factors such as set-up times, batch sizes, queue times, inventory, etc. The purpose of this paper is to develop, apply, and illustrate a new mathematical approach to solve cell formation problems in CMS. Specifically, a procedure using the fuzzy relations concept and the production flow analysis is formulated and utilized to design cell formations; the application of this matrix formulation - based algorithm, employing a similarity coefficient between machines and components (parts), is illustrated through a numerical example. From a binary machine - component matrix, the machine groups and part families are determined. Based on the fuzzy component - cell relation, parts are assigned to appropriate cells. A unique advantage of the proposed method is that the cell formation obtained is very flexible because each part can be assigned to different cells with different degrees of relation. A significant original contribution of this research is the ability to design cell formation involving multiple criteria/attributes where the number of cells to be formed is unknown. This is very useful for production engineers and managers. Suggestions for future research include addressing cell formation in which the system information is a combination of deterministic, probabilistic, and fuzzy values; addressing cell formation through a combination of fuzzy set theory (to handle the imprecise and vague information), neural networks (to capture the pattern), and genetic algorithms (to reach the global optimum). [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Production Engineering & Management is the property of Production Engineering Institute (PEI), University of Maribor 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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| Items | – Name: Title Label: Title Group: Ti Data: CELL FORMATION DESIGN USING FUZZY RELATIONS BASED ON A SIMILARITY COEFFICIENT. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22G%2E%2C+Naadimuthu%22">G., Naadimuthu</searchLink><relatesTo>1</relatesTo><i> naadi@fdu.edu</i><br /><searchLink fieldCode="AR" term="%22P%2E%2C+Gultom%22">P., Gultom</searchLink><relatesTo>2</relatesTo><i> gultom@ksu.edu</i><br /><searchLink fieldCode="AR" term="%22S%2E%2C+Lee+E%2E%22">S., Lee E.</searchLink><relatesTo>2</relatesTo><i> lee@ksu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Advances+in+Production+Engineering+%26+Management%22">Advances in Production Engineering & Management</searchLink>. Jun2011, Vol. 6 Issue 2, p77-86. 10p. 4 Diagrams, 3 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fuzzy+relational+calculus%22">Fuzzy relational calculus</searchLink><br /><searchLink fieldCode="DE" term="%22Coefficient+of+concordance%22">Coefficient of concordance</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+cells%22">Manufacturing cells</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+analytic+methods%22">Matrix analytic methods</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+derivatives%22">Matrix derivatives</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Based on literature survey results, the cell formation design is one of the most important operations in cellular manufacturing systems (CMS), since it impacts various factors such as set-up times, batch sizes, queue times, inventory, etc. The purpose of this paper is to develop, apply, and illustrate a new mathematical approach to solve cell formation problems in CMS. Specifically, a procedure using the fuzzy relations concept and the production flow analysis is formulated and utilized to design cell formations; the application of this matrix formulation - based algorithm, employing a similarity coefficient between machines and components (parts), is illustrated through a numerical example. From a binary machine - component matrix, the machine groups and part families are determined. Based on the fuzzy component - cell relation, parts are assigned to appropriate cells. A unique advantage of the proposed method is that the cell formation obtained is very flexible because each part can be assigned to different cells with different degrees of relation. A significant original contribution of this research is the ability to design cell formation involving multiple criteria/attributes where the number of cells to be formed is unknown. This is very useful for production engineers and managers. Suggestions for future research include addressing cell formation in which the system information is a combination of deterministic, probabilistic, and fuzzy values; addressing cell formation through a combination of fuzzy set theory (to handle the imprecise and vague information), neural networks (to capture the pattern), and genetic algorithms (to reach the global optimum). [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Advances in Production Engineering & Management is the property of Production Engineering Institute (PEI), University of Maribor 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 77 Subjects: – SubjectFull: Fuzzy relational calculus Type: general – SubjectFull: Coefficient of concordance Type: general – SubjectFull: Manufacturing cells Type: general – SubjectFull: Matrix analytic methods Type: general – SubjectFull: Matrix derivatives Type: general Titles: – TitleFull: CELL FORMATION DESIGN USING FUZZY RELATIONS BASED ON A SIMILARITY COEFFICIENT. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: G., Naadimuthu – PersonEntity: Name: NameFull: P., Gultom – PersonEntity: Name: NameFull: S., Lee E. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 18546250 Numbering: – Type: volume Value: 6 – Type: issue Value: 2 Titles: – TitleFull: Advances in Production Engineering & Management Type: main |
| ResultId | 1 |
