Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches

Hoseini, SA; Fallahpour, A; Wong, KY; Mahdiyar, A; Saberi, M; Durdyev, S

Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches

Hoseini, SA Fallahpour, A Wong, KY Mahdiyar, A Saberi, M

Durdyev, S

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sustainability, 2021, 13, (3), pp. 1-19
Issue Date:: 2021-01-29

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Field	Value	Language
dc.contributor.author	Hoseini, SA
dc.contributor.author	Fallahpour, A
dc.contributor.author	Wong, KY
dc.contributor.author	Mahdiyar, A
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Durdyev, S
dc.date.accessioned	2022-05-06T06:09:17Z
dc.date.available	2022-05-06T06:09:17Z
dc.date.issued	2021-01-29
dc.identifier.citation	Sustainability, 2021, 13, (3), pp. 1-19
dc.identifier.issn	2071-1050
dc.identifier.issn	2071-1050
dc.identifier.uri	http://hdl.handle.net/10453/157095
dc.description.abstract	Due to increase in the public and stakeholders’ awareness regarding economic, environmental, and social issues, the construction industry tends to follow the sustainability policies and practices in supply chain management. Hence, one of the most crucial aspects for a construction company in this regard is sustainable supplier selection, and, to this end, an accurate and reliable model is required. In this paper a hybrid fuzzy best-worst method and fuzzy inference system model is developed for sustainable supplier selection. In the first phase of this study, after determining 19 criteria in three main aspects, the final weight of each aspect and criterion is obtained using fuzzy best-worst method approach. In the second phase, the most sustainable supplier is selected by running the weighted fuzzy inference system both in aspect and criterion level, providing more accurate results compared to the use of other available models. Finally, two different tests are employed to validate the results and evaluate the robustness of the proposed model. The novel developed model enables the decision-maker to simulate the decision-making process, reduce the calculations loads, consider a large number of criteria in decision making, and resolve the inherited uncertainties in experts’ responses.
dc.language	English
dc.publisher	MDPI AG
dc.relation.ispartof	Sustainability
dc.relation.isbasedon	10.3390/su13031413
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	12 Built Environment and Design
dc.title	Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	12 Built Environment and Design
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	*
pubs.consider-herdc	false
dc.date.updated	2022-05-06T06:09:15Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	3

Abstract:

Due to increase in the public and stakeholders’ awareness regarding economic, environmental, and social issues, the construction industry tends to follow the sustainability policies and practices in supply chain management. Hence, one of the most crucial aspects for a construction company in this regard is sustainable supplier selection, and, to this end, an accurate and reliable model is required. In this paper a hybrid fuzzy best-worst method and fuzzy inference system model is developed for sustainable supplier selection. In the first phase of this study, after determining 19 criteria in three main aspects, the final weight of each aspect and criterion is obtained using fuzzy best-worst method approach. In the second phase, the most sustainable supplier is selected by running the weighted fuzzy inference system both in aspect and criterion level, providing more accurate results compared to the use of other available models. Finally, two different tests are employed to validate the results and evaluate the robustness of the proposed model. The novel developed model enables the decision-maker to simulate the decision-making process, reduce the calculations loads, consider a large number of criteria in decision making, and resolve the inherited uncertainties in experts’ responses.

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http://hdl.handle.net/10453/157095