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The averaging operator stands as a fundamental and widely employed aggregation tool in decision-making processes. This chapter delves into the realm of generalized orthopair fuzzy numbers, a domain typically governed by a t-norms (TNs) and their corresponding t-conorms (TCNs). Archimedean TN and TCN establish a versatile foundation for intersection and union operations within the context of fuzzy sets. In this context, we discuss the q-rung orthopair fuzzy Archimedean weighted averaging (q-ROFAWA) and q-rung orthopair fuzzy Archimedean ordered weighted averaging (q-ROFOWA) operators, extending the applicability of these operators. Additionally, we propose a q-rung orthopair fuzzy Archimedean ordinal priority approach (q-ROFAOPA) for the systematic evaluation of attribute weights. The culmination of this work manifests in a multi-attribute group decision-making (MAGDM) approach, along with its practical implementation, offering a comprehensive solution for decision-making scenarios.

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Archimedean Averaging Operators with Linear Programming Model for Generalized Orthopair Fuzzy Decision-Making

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  • 发表日期：

    2025年

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  • Sukhwinder Singh Rawat,
  • Komal

摘要

The averaging operator stands as a fundamental and widely employed aggregation tool in decision-making processes. This chapter delves into the realm of generalized orthopair fuzzy numbers, a domain typically governed by a t-norms (TNs) and their corresponding t-conorms (TCNs). Archimedean TN and TCN establish a versatile foundation for intersection and union operations within the context of fuzzy sets. In this context, we discuss the q-rung orthopair fuzzy Archimedean weighted averaging (q-ROFAWA) and q-rung orthopair fuzzy Archimedean ordered weighted averaging (q-ROFOWA) operators, extending the applicability of these operators. Additionally, we propose a q-rung orthopair fuzzy Archimedean ordinal priority approach (q-ROFAOPA) for the systematic evaluation of attribute weights. The culmination of this work manifests in a multi-attribute group decision-making (MAGDM) approach, along with its practical implementation, offering a comprehensive solution for decision-making scenarios.