Optimized data manipulation methods for intensive hesitant fuzzy set with applications to decision making

Hao, Zhinan; Zhang, Xu; Zhao, Hua; Su, Zhan

doi:10.1016/j.ins.2021.08.063

Cited by 11 publications

(1 citation statement)

References 38 publications

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“…Due to the complex uncertainty of environmental conditions and the cognitive limitations of DMs, traditional quantitative methods usually cannot accurately describe decision-making problems. Many scholars have focused on fuzzy sets (FSs) [3] [4] [5] and their extensions (e.g., intuitionistic fuzzy sets (IFSs) [6] [7] [8], hesitant fuzzy sets (HFSs) [9] [10] [11], picture fuzzy sets (PFSs) [12] [13] [14]), and fuzzy rough sets (FRSs) and so on [15] [16]) to solve this issue. However, compared those extensions, neutrosophic sets (NSs) [17] [18] [19] characterized by truth-membership, indeterminacy-membership, and falsity-membership function can better describe the fuzzy uncertainty of decision-making provided by DMs.…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Group Decision-Making Method Based on an Improved Single-Valued Neutrosophic Hamacher Weighted Average Operator and Grey Relational Analysis

Mei¹,

Yang²,

Chen³

2023

JCC

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This paper proposes a multi-criteria decision-making (MCGDM) method based on the improved single-valued neutrosophic Hamacher weighted averaging (ISNHWA) operator and grey relational analysis (GRA) to overcome the limitations of present methods based on aggregation operators. First, the limitations of several existing single-valued neutrosophic weighted averaging aggregation operators (i.e., the single-valued neutrosophic weighted averaging, single-valued neutrosophic weighted algebraic averaging, single-valued neutrosophic weighted Einstein averaging, single-valued neutrosophic Frank weighted averaging, and single-valued neutrosophic Hamacher weighted averaging operators), which can produce some indeterminate terms in the aggregation process, are discussed. Second, an ISNHWA operator was developed to overcome the limitations of existing operators. Third, the properties of the proposed operator, including idempotency, boundedness, monotonicity, and commutativity, were analyzed. Application examples confirmed that the ISNHWA operator and the proposed MCGDM method are rational and effective. The proposed improved ISNHWA operator and MCGDM method can overcome the indeterminate results in some special cases in existing single-valued neutrosophic weighted averaging aggregation operators and MCGDM methods.

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