Forecasting policies for scheduling a stochastic due date job shop.
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| Title: | Forecasting policies for scheduling a stochastic due date job shop. |
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| Authors: | Singer, Marcos1 singer@volcan.facea.puc.cl |
| Source: | International Journal of Production Research. 10/10/2000, Vol. 38 Issue 15, p3623-3637. 15p. |
| Subjects: | Production scheduling, Production control, Production planning, Manufacturing industries, Manufacturing processes |
| Abstract: | This work studies the problem of scheduling a production plant subject to uncertain processing times that may arise, e.g. from the variability of human labour or the possibility of machine breakdowns. The problem is modelled as a job shop with random processing times, where the expected total weighted tardiness must be minimized. A heuristic is proposed that amplifies the expected processing times by a selected factor, which are used as input for a deterministic scheduling algorithm. The quality of a particular solution is measured using a risk averse penalty function combining the expected deviation and the worst case deviation from the optimal schedule. Computational tests show that the technique improves the performance of the deterministic algorithm by 25% when compared with using the unscaled expected processing times as inputs. [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: 3815340 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Forecasting policies for scheduling a stochastic due date job shop. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Singer%2C+Marcos%22">Singer, Marcos</searchLink><relatesTo>1</relatesTo><i> singer@volcan.facea.puc.cl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. 10/10/2000, Vol. 38 Issue 15, p3623-3637. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Production+control%22">Production control</searchLink><br /><searchLink fieldCode="DE" term="%22Production+planning%22">Production planning</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+industries%22">Manufacturing industries</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This work studies the problem of scheduling a production plant subject to uncertain processing times that may arise, e.g. from the variability of human labour or the possibility of machine breakdowns. The problem is modelled as a job shop with random processing times, where the expected total weighted tardiness must be minimized. A heuristic is proposed that amplifies the expected processing times by a selected factor, which are used as input for a deterministic scheduling algorithm. The quality of a particular solution is measured using a risk averse penalty function combining the expected deviation and the worst case deviation from the optimal schedule. Computational tests show that the technique improves the performance of the deterministic algorithm by 25% when compared with using the unscaled expected processing times as inputs. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=3815340 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/002075400422824 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 3623 Subjects: – SubjectFull: Production scheduling Type: general – SubjectFull: Production control Type: general – SubjectFull: Production planning Type: general – SubjectFull: Manufacturing industries Type: general – SubjectFull: Manufacturing processes Type: general Titles: – TitleFull: Forecasting policies for scheduling a stochastic due date job shop. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singer, Marcos IsPartOfRelationships: – BibEntity: Dates: – D: 10 M: 10 Text: 10/10/2000 Type: published Y: 2000 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 38 – Type: issue Value: 15 Titles: – TitleFull: International Journal of Production Research Type: main |
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