Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems

The linear Diophantine fuzzy set (LDFS) has been proved to be an efficient tool in expressing decision maker (DM) evaluation values in multicriteria decision-making (MCDM) procedure. To more effectively represent DMs’ evaluation information in complicated MCDM process, this paper proposes a MCDM met...

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Main Authors: Aiyared Iampan, Gustavo Santos García, Muhammad Riaz, Hafiz Muhammad Athar Farid, Ronnason Chinram
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/2021/5548033
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spelling doaj-1ab7f37d7f534455b81830197228091e2021-07-26T00:35:14ZengHindawi LimitedJournal of Mathematics2314-47852021-01-01202110.1155/2021/5548033Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making ProblemsAiyared Iampan0Gustavo Santos García1Muhammad Riaz2Hafiz Muhammad Athar Farid3Ronnason Chinram4Department of MathematicsDepartment of Economics and Economic HistoryDepartment of MathematicsDepartment of MathematicsAlgebra and Applications Research UnitThe linear Diophantine fuzzy set (LDFS) has been proved to be an efficient tool in expressing decision maker (DM) evaluation values in multicriteria decision-making (MCDM) procedure. To more effectively represent DMs’ evaluation information in complicated MCDM process, this paper proposes a MCDM method based on proposed novel aggregation operators (AOs) under linear Diophantine fuzzy set (LDFS). A q-Rung orthopair fuzzy set (q-ROFS), Pythagorean fuzzy set (PFS), and intuitionistic fuzzy set (IFS) are rudimentary concepts in computational intelligence, which have diverse applications in modeling uncertainty and MCDM. Unfortunately, these theories have their own limitations related to the membership and nonmembership grades. The linear Diophantine fuzzy set (LDFS) is a new approach towards uncertainty which has the ability to relax the strict constraints of IFS, PFS, and q–ROFS by considering reference/control parameters. LDFS provides an appropriate way to the decision experts (DEs) in order to deal with vague and uncertain information in a comprehensive way. Under these environments, we introduce several AOs named as linear Diophantine fuzzy Einstein weighted averaging (LDFEWA) operator, linear Diophantine fuzzy Einstein ordered weighted averaging (LDFEOWA) operator, linear Diophantine fuzzy Einstein weighted geometric (LDFEWG) operator, and linear Diophantine fuzzy Einstein ordered weighted geometric (LDFEOWG) operator. We investigate certain characteristics and operational laws with some illustrations. Ultimately, an innovative approach for MCDM under the linear Diophantine fuzzy information is examined by implementing suggested aggregation operators. A useful example related to a country’s national health administration (NHA) to create a fully developed postacute care (PAC) model network for the health recovery of patients suffering from cerebrovascular diseases (CVDs) is exhibited to specify the practicability and efficacy of the intended approach.http://dx.doi.org/10.1155/2021/5548033
collection DOAJ
language English
format Article
sources DOAJ
author Aiyared Iampan
Gustavo Santos García
Muhammad Riaz
Hafiz Muhammad Athar Farid
Ronnason Chinram
spellingShingle Aiyared Iampan
Gustavo Santos García
Muhammad Riaz
Hafiz Muhammad Athar Farid
Ronnason Chinram
Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems
Journal of Mathematics
author_facet Aiyared Iampan
Gustavo Santos García
Muhammad Riaz
Hafiz Muhammad Athar Farid
Ronnason Chinram
author_sort Aiyared Iampan
title Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems
title_short Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems
title_full Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems
title_fullStr Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems
title_full_unstemmed Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems
title_sort linear diophantine fuzzy einstein aggregation operators for multi-criteria decision-making problems
publisher Hindawi Limited
series Journal of Mathematics
issn 2314-4785
publishDate 2021-01-01
description The linear Diophantine fuzzy set (LDFS) has been proved to be an efficient tool in expressing decision maker (DM) evaluation values in multicriteria decision-making (MCDM) procedure. To more effectively represent DMs’ evaluation information in complicated MCDM process, this paper proposes a MCDM method based on proposed novel aggregation operators (AOs) under linear Diophantine fuzzy set (LDFS). A q-Rung orthopair fuzzy set (q-ROFS), Pythagorean fuzzy set (PFS), and intuitionistic fuzzy set (IFS) are rudimentary concepts in computational intelligence, which have diverse applications in modeling uncertainty and MCDM. Unfortunately, these theories have their own limitations related to the membership and nonmembership grades. The linear Diophantine fuzzy set (LDFS) is a new approach towards uncertainty which has the ability to relax the strict constraints of IFS, PFS, and q–ROFS by considering reference/control parameters. LDFS provides an appropriate way to the decision experts (DEs) in order to deal with vague and uncertain information in a comprehensive way. Under these environments, we introduce several AOs named as linear Diophantine fuzzy Einstein weighted averaging (LDFEWA) operator, linear Diophantine fuzzy Einstein ordered weighted averaging (LDFEOWA) operator, linear Diophantine fuzzy Einstein weighted geometric (LDFEWG) operator, and linear Diophantine fuzzy Einstein ordered weighted geometric (LDFEOWG) operator. We investigate certain characteristics and operational laws with some illustrations. Ultimately, an innovative approach for MCDM under the linear Diophantine fuzzy information is examined by implementing suggested aggregation operators. A useful example related to a country’s national health administration (NHA) to create a fully developed postacute care (PAC) model network for the health recovery of patients suffering from cerebrovascular diseases (CVDs) is exhibited to specify the practicability and efficacy of the intended approach.
url http://dx.doi.org/10.1155/2021/5548033
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