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dc.contributor.authorAtiqe Ur Rahmanen_US
dc.contributor.authorMuhammad Saeeden_US
dc.contributor.authorMazin Abed Mohammeden_US
dc.contributor.authorArnab Majumdaren_US
dc.contributor.authorOrawit Thinnukoolen_US
dc.description.abstractThe suppliers play a significant role in supply chain management. In supplier selection, factors like market-based exposure, community-based reputation, trust-based status, etc., must be considered, along with the opinions of hired experts. These factors are usually termed as rough information. Most of the literature has disregarded such factors, which may lead to a biased selection. In this study, linguistic variables in terms of triangular fuzzy numbers (TrFn) are used to manage such kind of rough information, then the rough approximations of the fuzzy hypersoft set (FHS-set) are characterized which are capable of handling such informational uncertainties. The FHS-set is more flexible as well as consistent as it tackles the limitation of fuzzy soft sets regarding categorizing parameters into their related sub-classes having their sub-parametric values. Based on these rough approximations, an algorithm is proposed for the optimal selection of suppliers by managing experts’ opinions and rough information collectively in the form of TrFn-based linguistic variables. To have a discrete decision, a signed distance method is employed to transform the TrFn-based opinions of experts into fuzzy grades. The proposed algorithm is corroborated with the help of a multi-criteria decision-making application to choose the best supplier for real estate builders. The beneficial facets of the put forward study are appraised through its structural comparison with few existing related approaches. The presented approach is consistent as it is capable to manage rough information and expert’s opinions about suppliers collectively by using rough approximations of FHS-set.en_US
dc.titleSupplier Selection through Multicriteria Decision-Making Algorithmic Approach Based on Rough Approximation of Fuzzy Hypersoft Sets for Construction Projecten_US
article.volume12en_US Of Anbaren_US of Management and Technology Lahoreen_US College Londonen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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