Linguistic Fuzzy Process Capability Analysis Based on Pythagorean and q-Rung Orthopair Fuzzy Sets.
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| Title: | Linguistic Fuzzy Process Capability Analysis Based on Pythagorean and q-Rung Orthopair Fuzzy Sets. |
|---|---|
| Authors: | Rodríguez-Álvarez, José L1 (AUTHOR) luis.rodriguez@itsrll.edu.mx, García-Alcaraz, Jorge L2 (AUTHOR), Díaz-Reza, José R2 (AUTHOR), Navarrete-Molina, Cayetano3 (AUTHOR), Grijalva-Avila, Gerardo4 (AUTHOR) |
| Source: | Journal of Intelligent & Fuzzy Systems. Apr2026, Vol. 50 Issue 4, p1340-1362. 23p. |
| Subjects: | Process capability, Fuzzy sets, Statistical process control, Quality control, Statistics |
| Abstract: | Process Capability Analysis (PCA) is a widely used statistical method for assessing whether a process can meet customer-defined specification limits (SLs). However, traditional PCA faces limitations when dealing with rigidly defined SLs or uncertain data types. To address these challenges, fuzzy set theory (FST) has been extensively applied, enhancing PCA sensitivity by capturing imprecision in human judgments. This study proposes a novel PCA method based on Pythagorean fuzzy sets (PFSs) and q-rung orthopair fuzzy sets (q-ROFSs), incorporating two linguistic terms (LTs) for quality evaluation and hesitation. Unlike previous studies, our method replaces traditional α-cuts with a hesitancy-based approach and employs q -ROFSs to represent uncertainty comprehensively. In a real-world case study in the meat industry, the proposed method demonstrated a long-term capability index (Ppk) ranging from 0.42 to 0.73 and a short-term capability index (Cpk) ranging from 1.01 to 1.43. At the same time, traditional approaches showed wider and less informative ranges. These results indicate enhanced sensitivity and robustness in process performance assessment. The novelty of this approach lies in combining dual linguistic terms and the hesitancy estimation via q-ROFSs, offering a more nuanced and accurate depiction of process capability, particularly in contexts involving imprecise human evaluations. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. 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: 192656091 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Linguistic Fuzzy Process Capability Analysis Based on Pythagorean and q-Rung Orthopair Fuzzy Sets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rodríguez-Álvarez%2C+José+L%22">Rodríguez-Álvarez, José L</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> luis.rodriguez@itsrll.edu.mx</i><br /><searchLink fieldCode="AR" term="%22García-Alcaraz%2C+Jorge+L%22">García-Alcaraz, Jorge L</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Díaz-Reza%2C+José+R%22">Díaz-Reza, José R</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Navarrete-Molina%2C+Cayetano%22">Navarrete-Molina, Cayetano</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Grijalva-Avila%2C+Gerardo%22">Grijalva-Avila, Gerardo</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+%26+Fuzzy+Systems%22">Journal of Intelligent & Fuzzy Systems</searchLink>. Apr2026, Vol. 50 Issue 4, p1340-1362. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Process+capability%22">Process capability</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+sets%22">Fuzzy sets</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+process+control%22">Statistical process control</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control%22">Quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Process Capability Analysis (PCA) is a widely used statistical method for assessing whether a process can meet customer-defined specification limits (SLs). However, traditional PCA faces limitations when dealing with rigidly defined SLs or uncertain data types. To address these challenges, fuzzy set theory (FST) has been extensively applied, enhancing PCA sensitivity by capturing imprecision in human judgments. This study proposes a novel PCA method based on Pythagorean fuzzy sets (PFSs) and q-rung orthopair fuzzy sets (q-ROFSs), incorporating two linguistic terms (LTs) for quality evaluation and hesitation. Unlike previous studies, our method replaces traditional α-cuts with a hesitancy-based approach and employs q -ROFSs to represent uncertainty comprehensively. In a real-world case study in the meat industry, the proposed method demonstrated a long-term capability index (Ppk) ranging from 0.42 to 0.73 and a short-term capability index (Cpk) ranging from 1.01 to 1.43. At the same time, traditional approaches showed wider and less informative ranges. These results indicate enhanced sensitivity and robustness in process performance assessment. The novelty of this approach lies in combining dual linguistic terms and the hesitancy estimation via q-ROFSs, offering a more nuanced and accurate depiction of process capability, particularly in contexts involving imprecise human evaluations. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. 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: 10.1177/18758967251374197 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1340 Subjects: – SubjectFull: Process capability Type: general – SubjectFull: Fuzzy sets Type: general – SubjectFull: Statistical process control Type: general – SubjectFull: Quality control Type: general – SubjectFull: Statistics Type: general Titles: – TitleFull: Linguistic Fuzzy Process Capability Analysis Based on Pythagorean and q-Rung Orthopair Fuzzy Sets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rodríguez-Álvarez, José L – PersonEntity: Name: NameFull: García-Alcaraz, Jorge L – PersonEntity: Name: NameFull: Díaz-Reza, José R – PersonEntity: Name: NameFull: Navarrete-Molina, Cayetano – PersonEntity: Name: NameFull: Grijalva-Avila, Gerardo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10641246 Numbering: – Type: volume Value: 50 – Type: issue Value: 4 Titles: – TitleFull: Journal of Intelligent & Fuzzy Systems Type: main |
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