OWA operators and probabilities under hypersoft set environments

Saqlain, M; Merigó, JM; Kumam, P; Pamucar, D

OWA operators and probabilities under hypersoft set environments

Saqlain, M

Merigó, JM Kumam, P Pamucar, D

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Systems and Soft Computing, 2025, 7
Issue Date:: 2025-12-01

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Field	Value	Language
dc.contributor.author	Saqlain, M https://orcid.org/0000-0003-3617-6043
dc.contributor.author	Merigó, JM
dc.contributor.author	Kumam, P
dc.contributor.author	Pamucar, D
dc.date.accessioned	2026-01-30T00:16:02Z
dc.date.available	2026-01-30T00:16:02Z
dc.date.issued	2025-12-01
dc.identifier.citation	Systems and Soft Computing, 2025, 7
dc.identifier.issn	2772-9419
dc.identifier.issn	2772-9419
dc.identifier.uri	http://hdl.handle.net/10453/192568
dc.description.abstract	This study proposes novel extensions to overcome the limitations of classical aggregation methods, namely ordered weighted averaging (OWA) and probabilistic OWA (POWA) operators, in handling hierarchical or subdivided attributes under uncertainty within the hypersoft set (HS) framework, resulting in the hypersoft set-based OWA (HS-OWA) and hypersoft set-based POWA (HS-POWA) operators. These extensions (HS-OWA and HS-POWA operators) preserve sub-attribute information, enhance decision accuracy, and handle uncertainty, including fuzzy, intuitionistic, and neutrosophic data. We formalize the mathematical definitions and theoretical properties of HS-OWA and HS-POWA, demonstrating their practical applicability through a case study of sustainable wastewater treatment method selection. Additionally, we generalize the proposed operators under various fuzzy extensions, including intuitionistic fuzzy sets (IFS), pythagorean fuzzy sets (PFS), q-Rung orthopair fuzzy sets (q-ROFS), and neutrosophic sets (NS), allowing flexible modeling of uncertainty, hesitation, and conflict in expert assessments. The results from our study validate the superiority of the proposed framework in aggregating distributed evaluations while preserving semantic depth and interoperability. The proposed operators are effective in complex multi-criteria and group decision-making problems, such as sustainable technology assessment and policy-making, and provide a robust framework for future research in dynamic and large-scale MCDM applications.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Systems and Soft Computing
dc.relation.isbasedon	10.1016/j.sasc.2025.200408
dc.rights	info:eu-repo/semantics/openAccess
dc.title	OWA operators and probabilities under hypersoft set environments
dc.type	Journal Article
utslib.citation.volume	7
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-01-30T00:16:01Z
pubs.publication-status	Published
pubs.volume	7

Abstract:

This study proposes novel extensions to overcome the limitations of classical aggregation methods, namely ordered weighted averaging (OWA) and probabilistic OWA (POWA) operators, in handling hierarchical or subdivided attributes under uncertainty within the hypersoft set (HS) framework, resulting in the hypersoft set-based OWA (HS-OWA) and hypersoft set-based POWA (HS-POWA) operators. These extensions (HS-OWA and HS-POWA operators) preserve sub-attribute information, enhance decision accuracy, and handle uncertainty, including fuzzy, intuitionistic, and neutrosophic data. We formalize the mathematical definitions and theoretical properties of HS-OWA and HS-POWA, demonstrating their practical applicability through a case study of sustainable wastewater treatment method selection. Additionally, we generalize the proposed operators under various fuzzy extensions, including intuitionistic fuzzy sets (IFS), pythagorean fuzzy sets (PFS), q-Rung orthopair fuzzy sets (q-ROFS), and neutrosophic sets (NS), allowing flexible modeling of uncertainty, hesitation, and conflict in expert assessments. The results from our study validate the superiority of the proposed framework in aggregating distributed evaluations while preserving semantic depth and interoperability. The proposed operators are effective in complex multi-criteria and group decision-making problems, such as sustainable technology assessment and policy-making, and provide a robust framework for future research in dynamic and large-scale MCDM applications.

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http://hdl.handle.net/10453/192568