Solving multi-objective optimization problem in Bipolar Hesitant Fuzzy environment.
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| Title: | Solving multi-objective optimization problem in Bipolar Hesitant Fuzzy environment. |
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| Authors: | Jana, Swarup1 (AUTHOR) swarupnsec@gmail.com, Islam, Sahidul1 (AUTHOR) |
| Source: | RAIRO: Operations Research (2804-7303). 2025, Vol. 59 Issue 6, p4023-4049. 27p. |
| Subjects: | Multi-objective optimization, Fuzzy algorithms, Uncertainty (Information theory), Fuzzy sets, Decision making, Production planning |
| Abstract: | Multi-objective optimization problems are pervasive in various fields, ranging from engineering and economics to environmental management and decision-making processes. These problems involve the simultaneous optimization of multiple conflicting objectives, often leading to complex and non-linear relationships between decision variables. To tackle such intricate problems, this article introduces a novel approach: Bipolar Hesitant Fuzzy Optimization (BHFO) method. This method extends traditional fuzzy, hesitant fuzzy and bipolar fuzzy optimization techniques by incorporating bipolar hesitant fuzzy sets (BHFS), which allow decision-makers to assign degrees of hesitation and bipolarity to their preferences, reflecting the inherent uncertainty and ambiguity associated with real-world decision-making. This approach recognizes that decision-makers may not always be completely certain about their preferences, which is a common scenario in practical multi-objective optimization problems. In this article, we present the theoretical foundations of the BHFO method, including the representation of the parameter as generalized bipolar parabolic fuzzy numbers and operations on these numbers. The proposed approach empowers decision-makers to navigate the complexities of multi-objective optimization problems effectively, accommodating hesitant and bipolar preferences. Furthermore, we illustrate the application of the BHFO method by solving multi objective production planning problem and the result is compared with the other existing methods. [ABSTRACT FROM AUTHOR] |
| Copyright of RAIRO: Operations Research (2804-7303) is the property of EDP Sciences 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: 191891823 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Solving multi-objective optimization problem in Bipolar Hesitant Fuzzy environment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jana%2C+Swarup%22">Jana, Swarup</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> swarupnsec@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Islam%2C+Sahidul%22">Islam, Sahidul</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22RAIRO%3A+Operations+Research+%282804-7303%29%22">RAIRO: Operations Research (2804-7303)</searchLink>. 2025, Vol. 59 Issue 6, p4023-4049. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+algorithms%22">Fuzzy algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+sets%22">Fuzzy sets</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Production+planning%22">Production planning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multi-objective optimization problems are pervasive in various fields, ranging from engineering and economics to environmental management and decision-making processes. These problems involve the simultaneous optimization of multiple conflicting objectives, often leading to complex and non-linear relationships between decision variables. To tackle such intricate problems, this article introduces a novel approach: Bipolar Hesitant Fuzzy Optimization (BHFO) method. This method extends traditional fuzzy, hesitant fuzzy and bipolar fuzzy optimization techniques by incorporating bipolar hesitant fuzzy sets (BHFS), which allow decision-makers to assign degrees of hesitation and bipolarity to their preferences, reflecting the inherent uncertainty and ambiguity associated with real-world decision-making. This approach recognizes that decision-makers may not always be completely certain about their preferences, which is a common scenario in practical multi-objective optimization problems. In this article, we present the theoretical foundations of the BHFO method, including the representation of the parameter as generalized bipolar parabolic fuzzy numbers and operations on these numbers. The proposed approach empowers decision-makers to navigate the complexities of multi-objective optimization problems effectively, accommodating hesitant and bipolar preferences. Furthermore, we illustrate the application of the BHFO method by solving multi objective production planning problem and the result is compared with the other existing methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of RAIRO: Operations Research (2804-7303) is the property of EDP Sciences 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: 10.1051/ro/2025149 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 4023 Subjects: – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Fuzzy algorithms Type: general – SubjectFull: Uncertainty (Information theory) Type: general – SubjectFull: Fuzzy sets Type: general – SubjectFull: Decision making Type: general – SubjectFull: Production planning Type: general Titles: – TitleFull: Solving multi-objective optimization problem in Bipolar Hesitant Fuzzy environment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jana, Swarup – PersonEntity: Name: NameFull: Islam, Sahidul IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 28047303 Numbering: – Type: volume Value: 59 – Type: issue Value: 6 Titles: – TitleFull: RAIRO: Operations Research (2804-7303) Type: main |
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