Two-Stage EDAS Decision Approach with Probabilistic Hesitant Fuzzy Information

Krishankumar, R; Mishra, AR; Rani, P; Ecer, F; Zavadskas, EK; Ravichandran, KS; Gandomi, A

Two-Stage EDAS Decision Approach with Probabilistic Hesitant Fuzzy Information

Krishankumar, R Mishra, AR Rani, P Ecer, F Zavadskas, EK Ravichandran, KS Gandomi, A

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Publisher:: Vilnius University Press
Publication Type:: Journal Article
Citation:: Informatica, 2024, pp. 1-33
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Krishankumar, R
dc.contributor.author	Mishra, AR
dc.contributor.author	Rani, P
dc.contributor.author	Ecer, F
dc.contributor.author	Zavadskas, EK
dc.contributor.author	Ravichandran, KS
dc.contributor.author	Gandomi, A https://orcid.org/0000-0002-2798-0104
dc.date.accessioned	2025-04-09T05:03:57Z
dc.date.available	2025-04-09T05:03:57Z
dc.date.issued	2024-01-01
dc.identifier.citation	Informatica, 2024, pp. 1-33
dc.identifier.issn	0868-4952
dc.identifier.issn	1822-8844
dc.identifier.uri	http://hdl.handle.net/10453/186803
dc.description.abstract	<jats:p>This paper develops a two-stage decision approach with probabilistic hesitant fuzzy data. Research challenges in earlier models are: (i) the calculation of occurrence probability; (ii) imputation of missing elements; (iii) consideration of attitude and hesitation of experts during weight calculation; (iv) capturing of interdependencies among experts during aggregation; and (v) ranking of alternatives with resemblance to human cognition. Driven by these challenges, a new group decision-making model is proposed with integrate methods for data curation and decision-making. The usefulness and superiority of the model is realized via an illustrative example of a logistic service provider selection.</jats:p>
dc.language	en
dc.publisher	Vilnius University Press
dc.relation.ispartof	Informatica
dc.relation.isbasedon	10.15388/24-infor577
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	Two-Stage EDAS Decision Approach with Probabilistic Hesitant Fuzzy Information
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
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	2025-04-09T05:03:55Z
pubs.publication-status	Published

Abstract:

This paper develops a two-stage decision approach with probabilistic hesitant fuzzy data. Research challenges in earlier models are: (i) the calculation of occurrence probability; (ii) imputation of missing elements; (iii) consideration of attitude and hesitation of experts during weight calculation; (iv) capturing of interdependencies among experts during aggregation; and (v) ranking of alternatives with resemblance to human cognition. Driven by these challenges, a new group decision-making model is proposed with integrate methods for data curation and decision-making. The usefulness and superiority of the model is realized via an illustrative example of a logistic service provider selection.

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http://hdl.handle.net/10453/186803