Novel coefficients for improved robustness in multi-criteria decision analysis.
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| Title: | Novel coefficients for improved robustness in multi-criteria decision analysis. |
|---|---|
| Authors: | Paradowski, Bartosz1 (AUTHOR) bartosz-paradowski@zut.edu.pl, Wątróbski, Jarosław2 (AUTHOR) jaroslaw.watrobski@usz.edu.pl, Sałabun, Wojciech1,3 (AUTHOR) w.salabun@il-pib.pl |
| Source: | Artificial Intelligence Review. Oct2025, Vol. 58 Issue 10, p1-41. 41p. |
| Subjects: | Multiple criteria decision making, Statistical decision making, Decision making, TOPSIS method |
| Abstract: | In multi-criteria decision-making (MCDM), decision-makers face increasing complexity and the need for enhanced tools to facilitate informed and well-aligned decision outcomes. A critical challenge in MCDM is the determination of criteria weights, which significantly influence the final ranking of alternatives. While recent approaches aim to eliminate the need for explicit weight assignment, certain decision contexts necessitate their inclusion. This study introduces two novel coefficients, Rank Stability (RS) and Balance Point (BP), designed to provide deeper insights into the decision problem and its solution properties. Rank Stability quantifies the robustness of a solution against perturbations, while Balance Point evaluates the conditioning of the solution within the problem's structure. The decision problem is defined by a set of alternatives and criteria, where modifications to alternatives require a reassessment of the decision model. To examine the properties of these coefficients, this study employs simulation experiments utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods, alongside case-based analyses demonstrating their practical applications. Additionally, extreme cases of RS and BP values are explored to enhance interpretability for decision-makers. A real-world decision problem is further analyzed to illustrate the applicability of these coefficients and introduce a novel framework for comparing MCDM methodologies. This approach facilitates a more systematic and comprehensive assessment of MCDM methods, contributing to the advancement of decision-support tools. [ABSTRACT FROM AUTHOR] |
| Copyright of Artificial Intelligence Review 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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186469688 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Novel coefficients for improved robustness in multi-criteria decision analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Paradowski%2C+Bartosz%22">Paradowski, Bartosz</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bartosz-paradowski@zut.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Wątróbski%2C+Jarosław%22">Wątróbski, Jarosław</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jaroslaw.watrobski@usz.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Sałabun%2C+Wojciech%22">Sałabun, Wojciech</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> w.salabun@il-pib.pl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+Review%22">Artificial Intelligence Review</searchLink>. Oct2025, Vol. 58 Issue 10, p1-41. 41p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+decision+making%22">Statistical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22TOPSIS+method%22">TOPSIS method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In multi-criteria decision-making (MCDM), decision-makers face increasing complexity and the need for enhanced tools to facilitate informed and well-aligned decision outcomes. A critical challenge in MCDM is the determination of criteria weights, which significantly influence the final ranking of alternatives. While recent approaches aim to eliminate the need for explicit weight assignment, certain decision contexts necessitate their inclusion. This study introduces two novel coefficients, Rank Stability (RS) and Balance Point (BP), designed to provide deeper insights into the decision problem and its solution properties. Rank Stability quantifies the robustness of a solution against perturbations, while Balance Point evaluates the conditioning of the solution within the problem's structure. The decision problem is defined by a set of alternatives and criteria, where modifications to alternatives require a reassessment of the decision model. To examine the properties of these coefficients, this study employs simulation experiments utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods, alongside case-based analyses demonstrating their practical applications. Additionally, extreme cases of RS and BP values are explored to enhance interpretability for decision-makers. A real-world decision problem is further analyzed to illustrate the applicability of these coefficients and introduce a novel framework for comparing MCDM methodologies. This approach facilitates a more systematic and comprehensive assessment of MCDM methods, contributing to the advancement of decision-support tools. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Artificial Intelligence Review 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/s10462-025-11307-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 41 StartPage: 1 Subjects: – SubjectFull: Multiple criteria decision making Type: general – SubjectFull: Statistical decision making Type: general – SubjectFull: Decision making Type: general – SubjectFull: TOPSIS method Type: general Titles: – TitleFull: Novel coefficients for improved robustness in multi-criteria decision analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Paradowski, Bartosz – PersonEntity: Name: NameFull: Wątróbski, Jarosław – PersonEntity: Name: NameFull: Sałabun, Wojciech IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02692821 Numbering: – Type: volume Value: 58 – Type: issue Value: 10 Titles: – TitleFull: Artificial Intelligence Review Type: main |
| ResultId | 1 |
