Academic Journal
A two-stage group stochastic preference analysis based on best-worst method.
| Τίτλος: | A two-stage group stochastic preference analysis based on best-worst method. |
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| Συγγραφείς: | Dai, Ning, Zhou, Ligang, Wu, Qun |
| Πηγή: | Applied Intelligence; Nov2024, Vol. 54 Issue 22, p11233-11247, 15p |
| Θεματικοί όροι: | Group decision making, Monte Carlo method, Stochastic analysis, Decision making, Algorithms |
| Περίληψη: | This paper proposes an integrated approach to group decision-making (GDM) by using stochastic preference analysis (SPA) and best-worst method (BWM). BWM preparation algorithm is proposed to obtain the best and worst of experts and the relative importance degree. Meanwhile, expert weights model and expert's priority vector model are proposed. Furthermore, the stochastic composite rank acceptability index, stochastic composite expected priority vector, stochastic composite expected rank and stochastic composite confidence factor are developed based on SPA to describe the ranks of alternatives based on SPA. Finally, a group stochastic preference analysis-best worst method (GSPA-BWM) algorithm is developed by analyzing the judgments space through Monte Carlo simulation. The experts can use this method to choose some of the outcomes which they find most useful to make reliable decisions. Examples and comparison analyses show that the proposed method is effective. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Intelligence is the property of Springer Nature 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10489-024-05730-5 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: A two-stage group stochastic preference analysis based on best-worst method. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dai%2C+Ning%22">Dai, Ning</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Ligang%22">Zhou, Ligang</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Qun%22">Wu, Qun</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Intelligence; Nov2024, Vol. 54 Issue 22, p11233-11247, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Group+decision+making%22">Group decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+analysis%22">Stochastic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes an integrated approach to group decision-making (GDM) by using stochastic preference analysis (SPA) and best-worst method (BWM). BWM preparation algorithm is proposed to obtain the best and worst of experts and the relative importance degree. Meanwhile, expert weights model and expert's priority vector model are proposed. Furthermore, the stochastic composite rank acceptability index, stochastic composite expected priority vector, stochastic composite expected rank and stochastic composite confidence factor are developed based on SPA to describe the ranks of alternatives based on SPA. Finally, a group stochastic preference analysis-best worst method (GSPA-BWM) algorithm is developed by analyzing the judgments space through Monte Carlo simulation. The experts can use this method to choose some of the outcomes which they find most useful to make reliable decisions. Examples and comparison analyses show that the proposed method is effective. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Intelligence is the property of Springer Nature 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: 10.1007/s10489-024-05730-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 11233 Subjects: – SubjectFull: Group decision making Type: general – SubjectFull: Monte Carlo method Type: general – SubjectFull: Stochastic analysis Type: general – SubjectFull: Decision making Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: A two-stage group stochastic preference analysis based on best-worst method. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dai, Ning – PersonEntity: Name: NameFull: Zhou, Ligang – PersonEntity: Name: NameFull: Wu, Qun IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 11 Text: Nov2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 54 – Type: issue Value: 22 Titles: – TitleFull: Applied Intelligence Type: main |
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