Scheduling parallel serial-batch processing machines with incompatible job families, sequence-dependent setup times and arbitrary sizes.
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| Title: | Scheduling parallel serial-batch processing machines with incompatible job families, sequence-dependent setup times and arbitrary sizes. |
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| Authors: | Gahm, Christian1 (AUTHOR) christian.gahm@wiwi.uni-augsburg.de, Wahl, Stefan1 (AUTHOR), Tuma, Axel1 (AUTHOR) |
| Source: | International Journal of Production Research. Sep2022, Vol. 60 Issue 17, p5131-5154. 24p. 1 Diagram, 19 Charts, 1 Map. |
| Subjects: | Batch processing, Setup time, Parallel processing, Families, Machinery, Scheduling, Tardiness, Immunocomputers |
| Abstract: | The scheduling of (parallel) serial-batch processing machines is a task arising in many industrial sectors. In the metal-processing industry for instance, cutting operations are necessary to fabricate varying metal pieces out of large base slides. Here, the (cutting) jobs have individual, arbitrary base slide capacity requirements (sizes), individual processing times and due dates, and specific material requirements (i.e. each job belongs to one specific job family, whereby jobs of different families cannot be processed within the same batch and thus are incompatible). In addition, switching of base metal slides and material dependent adjustments of machine parameters cause sequence-dependent setup times. All these conditions need to be considered while minimising total weighted tardiness. For solving the scheduling problem, a mixed-integer program and several tailor-made construction heuristics (enhanced by local search mechanisms) are presented. The experimental results show that problem instances with up to five machines and 60 jobs can be tackled using the optimisation model. The experiments on small and large problem instances (with up to 400 jobs) show that a purposefully used batch capacity limitation improves the solution quality remarkably. Applying the best heuristic to the data of two real-world application cases shows its huge potential to increase delivery reliability. [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: 159022751 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Scheduling parallel serial-batch processing machines with incompatible job families, sequence-dependent setup times and arbitrary sizes. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gahm%2C+Christian%22">Gahm, Christian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> christian.gahm@wiwi.uni-augsburg.de</i><br /><searchLink fieldCode="AR" term="%22Wahl%2C+Stefan%22">Wahl, Stefan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tuma%2C+Axel%22">Tuma, Axel</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>. Sep2022, Vol. 60 Issue 17, p5131-5154. 24p. 1 Diagram, 19 Charts, 1 Map. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Batch+processing%22">Batch processing</searchLink><br /><searchLink fieldCode="DE" term="%22Setup+time%22">Setup time</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Families%22">Families</searchLink><br /><searchLink fieldCode="DE" term="%22Machinery%22">Machinery</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Tardiness%22">Tardiness</searchLink><br /><searchLink fieldCode="DE" term="%22Immunocomputers%22">Immunocomputers</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The scheduling of (parallel) serial-batch processing machines is a task arising in many industrial sectors. In the metal-processing industry for instance, cutting operations are necessary to fabricate varying metal pieces out of large base slides. Here, the (cutting) jobs have individual, arbitrary base slide capacity requirements (sizes), individual processing times and due dates, and specific material requirements (i.e. each job belongs to one specific job family, whereby jobs of different families cannot be processed within the same batch and thus are incompatible). In addition, switching of base metal slides and material dependent adjustments of machine parameters cause sequence-dependent setup times. All these conditions need to be considered while minimising total weighted tardiness. For solving the scheduling problem, a mixed-integer program and several tailor-made construction heuristics (enhanced by local search mechanisms) are presented. The experimental results show that problem instances with up to five machines and 60 jobs can be tackled using the optimisation model. The experiments on small and large problem instances (with up to 400 jobs) show that a purposefully used batch capacity limitation improves the solution quality remarkably. Applying the best heuristic to the data of two real-world application cases shows its huge potential to increase delivery reliability. [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: 10.1080/00207543.2021.1951446 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 5131 Subjects: – SubjectFull: Batch processing Type: general – SubjectFull: Setup time Type: general – SubjectFull: Parallel processing Type: general – SubjectFull: Families Type: general – SubjectFull: Machinery Type: general – SubjectFull: Scheduling Type: general – SubjectFull: Tardiness Type: general – SubjectFull: Immunocomputers Type: general Titles: – TitleFull: Scheduling parallel serial-batch processing machines with incompatible job families, sequence-dependent setup times and arbitrary sizes. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gahm, Christian – PersonEntity: Name: NameFull: Wahl, Stefan – PersonEntity: Name: NameFull: Tuma, Axel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 60 – Type: issue Value: 17 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
