Linguistic-based meta-heuristic optimization model for flexible job shop scheduling.
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| Title: | Linguistic-based meta-heuristic optimization model for flexible job shop scheduling. |
|---|---|
| Authors: | Baykasoglu, Adil |
| Source: | International Journal of Production Research. 11/20/2002, Vol. 40 Issue 17, p4523-4543. 21p. 6 Diagrams, 5 Charts, 1 Graph. |
| Subjects: | Production scheduling, Job shops, Production management (Manufacturing), Production engineering |
| Abstract: | In this paper, a linguistic based meta-heuristic modelling and solution approach for solving the Flexible Job Shop Scheduling Problem (FJSSP) is presented. FJSSP is an extension of the classical job-shop scheduling problem. The present problem definition is to assign each operation to a machine out of a set of capable machines (the routing problem) and to order the operations on the machines (the sequencing problem), such that a predefined performance measure is optimized. The scope of the problem is widened by taking into account the alternative process plans for each part (process plan selection problem) in the present study. Moreover, instead of using operations to represent product processing requirements and machine processing capabilities, machine independent capability units, which are known as Resource Elements (RE), are used. Representation of unique and shared capability boundaries of machine tools and part processing requirements is possible via RE. Using REs in scheduling can also reduce the problem size. The FJSSP is presented as a grammar and the productions in the grammar are defined as controls. Using these controls and the Giffler and Thompson (1960) priority rule-based heuristic, a simulated annealing algorithm is developed to solve FJSSP. This novel approach simplifies the modelling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its solution. The results obtained from the computational study have shown that the proposed algorithm can solve this complex problem effectively within reasonable time. The results have also given some insights on the effect of the selection of dispatching rules and the flexibility level on the job shop performance. It is observed that the effect of dispatching rule selection on the job shop performance diminishes by increasing the job shop flexibility. [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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| Items | – Name: Title Label: Title Group: Ti Data: Linguistic-based meta-heuristic optimization model for flexible job shop scheduling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Baykasoglu%2C+Adil%22">Baykasoglu, Adil</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. 11/20/2002, Vol. 40 Issue 17, p4523-4543. 21p. 6 Diagrams, 5 Charts, 1 Graph. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Job+shops%22">Job shops</searchLink><br /><searchLink fieldCode="DE" term="%22Production+management+%28Manufacturing%29%22">Production management (Manufacturing)</searchLink><br /><searchLink fieldCode="DE" term="%22Production+engineering%22">Production engineering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, a linguistic based meta-heuristic modelling and solution approach for solving the Flexible Job Shop Scheduling Problem (FJSSP) is presented. FJSSP is an extension of the classical job-shop scheduling problem. The present problem definition is to assign each operation to a machine out of a set of capable machines (the routing problem) and to order the operations on the machines (the sequencing problem), such that a predefined performance measure is optimized. The scope of the problem is widened by taking into account the alternative process plans for each part (process plan selection problem) in the present study. Moreover, instead of using operations to represent product processing requirements and machine processing capabilities, machine independent capability units, which are known as Resource Elements (RE), are used. Representation of unique and shared capability boundaries of machine tools and part processing requirements is possible via RE. Using REs in scheduling can also reduce the problem size. The FJSSP is presented as a grammar and the productions in the grammar are defined as controls. Using these controls and the Giffler and Thompson (1960) priority rule-based heuristic, a simulated annealing algorithm is developed to solve FJSSP. This novel approach simplifies the modelling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its solution. The results obtained from the computational study have shown that the proposed algorithm can solve this complex problem effectively within reasonable time. The results have also given some insights on the effect of the selection of dispatching rules and the flexibility level on the job shop performance. It is observed that the effect of dispatching rule selection on the job shop performance diminishes by increasing the job shop flexibility. [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: 10.1080/00207540210147043 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 4523 Subjects: – SubjectFull: Production scheduling Type: general – SubjectFull: Job shops Type: general – SubjectFull: Production management (Manufacturing) Type: general – SubjectFull: Production engineering Type: general Titles: – TitleFull: Linguistic-based meta-heuristic optimization model for flexible job shop scheduling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Baykasoglu, Adil IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 11 Text: 11/20/2002 Type: published Y: 2002 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 40 – Type: issue Value: 17 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
