Analytics with stochastic optimisation: experimental results of demand uncertainty in process industries.
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| Title: | Analytics with stochastic optimisation: experimental results of demand uncertainty in process industries. |
|---|---|
| Authors: | Gupta, Narain1 (AUTHOR) goutam@iima.ac.in, Dutta, Goutam2 (AUTHOR), Mitra, Krishnendranath3 (AUTHOR), Kumar Tiwari, Manoj4 (AUTHOR) |
| Source: | International Journal of Production Research. Oct2023, Vol. 61 Issue 19, p6501-6518. 18p. 1 Diagram, 7 Charts, 7 Graphs. |
| Subjects: | Decision support systems, Stochastic programming, Distribution (Probability theory), Linear programming |
| Abstract: | This study reports the test results of a two-stage stochastic linear programming (SLP) model with recourse using a user-friendly generic decision support system (DSS) in a North American steel company. This model has the flexibility to configure multiple material facilities, activities and storage areas in a multi-period and multi-scenario environment. The value of stochastic solution (VSS) with a real-world example has a potential benefit of US$ 24.61 million. Experiments were designed according to the potential joint probability distribution scenarios and the magnitude of demand variability. Overall, 144 SLP optimisation model instances were solved across four industries, namely, steel, aluminium, polymer and pharmaceuticals. The academic contribution of this research is two-fold: first, the potential contribution to profit in a steel company using an SLP model; and second, the optimisation empirical experiments confirm a pattern that the VSS and expected value of perfect information (EVPI) increase with the increase in demand variability. This study has implications for practicing managers seeking business solutions with prescriptive analytics using stochastic optimisation-based DSS. This study will attract more industry attention to business solutions, and the prescriptive analytics discipline will garner more scholarly and industry attention. [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: 169927036 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Analytics with stochastic optimisation: experimental results of demand uncertainty in process industries. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gupta%2C+Narain%22">Gupta, Narain</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> goutam@iima.ac.in</i><br /><searchLink fieldCode="AR" term="%22Dutta%2C+Goutam%22">Dutta, Goutam</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mitra%2C+Krishnendranath%22">Mitra, Krishnendranath</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumar+Tiwari%2C+Manoj%22">Kumar Tiwari, Manoj</searchLink><relatesTo>4</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>. Oct2023, Vol. 61 Issue 19, p6501-6518. 18p. 1 Diagram, 7 Charts, 7 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+programming%22">Stochastic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+programming%22">Linear programming</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study reports the test results of a two-stage stochastic linear programming (SLP) model with recourse using a user-friendly generic decision support system (DSS) in a North American steel company. This model has the flexibility to configure multiple material facilities, activities and storage areas in a multi-period and multi-scenario environment. The value of stochastic solution (VSS) with a real-world example has a potential benefit of US$ 24.61 million. Experiments were designed according to the potential joint probability distribution scenarios and the magnitude of demand variability. Overall, 144 SLP optimisation model instances were solved across four industries, namely, steel, aluminium, polymer and pharmaceuticals. The academic contribution of this research is two-fold: first, the potential contribution to profit in a steel company using an SLP model; and second, the optimisation empirical experiments confirm a pattern that the VSS and expected value of perfect information (EVPI) increase with the increase in demand variability. This study has implications for practicing managers seeking business solutions with prescriptive analytics using stochastic optimisation-based DSS. This study will attract more industry attention to business solutions, and the prescriptive analytics discipline will garner more scholarly and industry attention. [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=169927036 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2022.2131926 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 6501 Subjects: – SubjectFull: Decision support systems Type: general – SubjectFull: Stochastic programming Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Linear programming Type: general Titles: – TitleFull: Analytics with stochastic optimisation: experimental results of demand uncertainty in process industries. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gupta, Narain – PersonEntity: Name: NameFull: Dutta, Goutam – PersonEntity: Name: NameFull: Mitra, Krishnendranath – PersonEntity: Name: NameFull: Kumar Tiwari, Manoj IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 61 – Type: issue Value: 19 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
