Einstein Aggregation Operator Technique for Linguistic Q-Rung Orthopair Fuzzy Hypersoft Set with Application to Sustainable Construction Industry
ieee（2025）
Abstract
The construction industry faces significant communication challenges due to unclear information, subjective interpretations, and the diversity of languages spoken in the field. Industry-specific context-dependent terms, and the need for accurate translations add further complexity. Additionally, inconsistencies in interpretation make it difficult to compile linguistic information from multiple sources into a unified framework. To address these challenges, in this article, we propose a novel theory known as linguistic q-rung orthopair fuzzy hypersoft set (Lq-ROFHS). To aggregate the various information, we defined some new weighted averaging and geometric operators by using Einstein t-norm operations. The fundamental properties of all these stated operators are derived in detail. To illustrate the method, a multi-criteria group decision making algorithm is proposed by using the stated operators and apply them to the case study related to the selection of the best construction company. To demonstrate the efficiency of the proposed algorithm, a comparative analysis between the proposed and the several existing studies is done by comparing the order of preference. The proposed approach is a significant advancement that will enable decision-makers to navigate the complexities of their choice with greater assurance and precision using a single tool.
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Key words
Aggregate Operator,Decision Making,Einstein Operator,Fuzzy set,Hypersoft set,Linguistic quantifiers,Optimization,Q-rung orthopair fuzzy hypersoft set
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