Szczegóły publikacji
Opis bibliograficzny
Local and global uncertainty quantification for a family of fuzzy multi-criteria sorting models / Boris Yatsalo, Alexander Radaev, Andrzej M. J. SKULIMOWSKI, LeSheng Jin, Luis Martínez // Information Sciences ; ISSN 0020-0255 . — 2027 — vol. 758 art. no. 123988, s. 1–34. — Bibliogr. s. 33–34, Abstr. — Publikacja dostępna online od: 2026-08-07
Autorzy (5)
- Yatsalo Boris
- Radaev Alexander
- AGHSkulimowski Andrzej Maciej
- Jin LeSheng
- Martínez Luis
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 169701 |
|---|---|
| Data dodania do BaDAP | 2026-09-29 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.ins.2026.123988 |
| Rok publikacji | 2027 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Information Sciences |
Abstract
This paper introduces a family of FMAVT-Sort models based on a fuzzy extension of the Multi-Criteria Decision Analysis (MCDA) method MAVT, alongside a framework for Uncertainty and Distinction Quantification (UQ and DQ) of the model outputs. The models differ in approaches to evaluating functions of fuzzy numbers (FNs), employing both precise and approximate techniques, and utilize four fuzzy ranking methods: defuzzification-based approaches (Centroid Index and Integral of Means), and two methods designed for ranking dependent FNs. The models are applied to a case study on university teacher evaluation. Assessment of local (within the case study) UQ and DQ and global (across a space of independent multicriteria problems) DQ is performed using algorithms that process output values from a large number of fuzzy multicriteria sorting problems simulated via Monte Carlo techniques in a fuzzy environment. A set of UQ indices, combined with a pre-uncertainty analysis stage, is implemented in a General Algorithm for Multicriteria Sorting, designed to support the decision-making process. The results demonstrate the effectiveness of UQ indices for decision support in multicriteria sorting and reveal significant distinctions among FMAVT-Sort models, underscoring the impact of model selection on final sorting outcomes. These findings hold scientific, methodological, and practical significance.
