An integrated decision model for cloud vendor selection using probabilistic linguistic information and unknown weights

Krishankumar, R; Supraja Nimmagadda, S; Mishra, AR; Pamucar, D; Ravichandran, KS; Gandomi, AH

An integrated decision model for cloud vendor selection using probabilistic linguistic information and unknown weights

Krishankumar, R Supraja Nimmagadda, S Mishra, AR Pamucar, D Ravichandran, KS Gandomi, AH

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Engineering Applications of Artificial Intelligence, 2022, 114
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Krishankumar, R
dc.contributor.author	Supraja Nimmagadda, S
dc.contributor.author	Mishra, AR
dc.contributor.author	Pamucar, D
dc.contributor.author	Ravichandran, KS
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-03-20T02:14:08Z
dc.date.available	2023-03-20T02:14:08Z
dc.date.issued	2022-09-01
dc.identifier.citation	Engineering Applications of Artificial Intelligence, 2022, 114
dc.identifier.issn	0952-1976
dc.identifier.issn	1873-6769
dc.identifier.uri	http://hdl.handle.net/10453/167693
dc.description.abstract	Because of the high competition among IT sectors, companies are planning to migrate to the cloud for effective growth and development. Driven by the importance of the cloud, new cloud vendors emerge each day to satisfy the demand of IT sectors. As a result, selection of an apt cloud vendor is critical. To this end, researchers have proposed different decision models, but these models do not effectively capture uncertainty during the decision-making process. To handle this issue, probabilistic linguistic information (PLI) is adopted in this paper, which associates occurrence probability to each term. Furthermore, weights of criteria are systematically determined using a deviation method, and cloud vendors are prioritized using a mathematical model under the PLI context. These methods are integrated to form the decision model, validated for its applicability using real case data from Cloud Armor. Finally, the advantages and weaknesses of the model are analyzed by using sensitivity analysis and comparison with extant models.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Engineering Applications of Artificial Intelligence
dc.relation.isbasedon	10.1016/j.engappai.2022.105114
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	An integrated decision model for cloud vendor selection using probabilistic linguistic information and unknown weights
dc.type	Journal Article
utslib.citation.volume	114
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-09-01T00:00:00+1000Z
dc.date.updated	2023-03-20T02:14:07Z
pubs.publication-status	Published
pubs.volume	114

Abstract:

Because of the high competition among IT sectors, companies are planning to migrate to the cloud for effective growth and development. Driven by the importance of the cloud, new cloud vendors emerge each day to satisfy the demand of IT sectors. As a result, selection of an apt cloud vendor is critical. To this end, researchers have proposed different decision models, but these models do not effectively capture uncertainty during the decision-making process. To handle this issue, probabilistic linguistic information (PLI) is adopted in this paper, which associates occurrence probability to each term. Furthermore, weights of criteria are systematically determined using a deviation method, and cloud vendors are prioritized using a mathematical model under the PLI context. These methods are integrated to form the decision model, validated for its applicability using real case data from Cloud Armor. Finally, the advantages and weaknesses of the model are analyzed by using sensitivity analysis and comparison with extant models.

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http://hdl.handle.net/10453/167693