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<p>The application of linguistic variables plays a crucial role in processing qualitative information for mathematical modeling and decision-making under uncertainty. The linguistic q-rung orthopair fuzzy (Lq-ROF) set, an advanced linguistic fuzzy framework, provides enhanced flexibility in decision-making problems that involve qualitative data by incorporating the rung parameter q. This study introduces four classes of Lq-ROF Hamy mean operators: the Lq-ROF Hamy mean operator, the Lq-ROF dual Hamy mean operator, the Lq-ROF weighted Hamy mean operator, and the Lq-ROF weighted dual Hamy mean operator, and investigates their fundamental properties. Additionally, the complex proportional assessment of alternatives (COPRAS) method is extended with Lq-ROF sets, presenting a novel approach for multi-attribute group decision-making under Lq-ROF information. The proposed framework is demonstrated through a case study on selecting an AI-based predictive maintenance system for manufacturing. The stability and effectiveness of the method are verified through parameter analysis and comparative evaluation with existing approaches.</p>

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Decision-making using COPRAS method with linguistic q-rung orthopair fuzzy sets and Hamy mean operators

  • 期刊论文

  • 发表日期：

    2025年8月19日

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  • Amjid Khan,
  • Jawad Ali

摘要

The application of linguistic variables plays a crucial role in processing qualitative information for mathematical modeling and decision-making under uncertainty. The linguistic q-rung orthopair fuzzy (Lq-ROF) set, an advanced linguistic fuzzy framework, provides enhanced flexibility in decision-making problems that involve qualitative data by incorporating the rung parameter q. This study introduces four classes of Lq-ROF Hamy mean operators: the Lq-ROF Hamy mean operator, the Lq-ROF dual Hamy mean operator, the Lq-ROF weighted Hamy mean operator, and the Lq-ROF weighted dual Hamy mean operator, and investigates their fundamental properties. Additionally, the complex proportional assessment of alternatives (COPRAS) method is extended with Lq-ROF sets, presenting a novel approach for multi-attribute group decision-making under Lq-ROF information. The proposed framework is demonstrated through a case study on selecting an AI-based predictive maintenance system for manufacturing. The stability and effectiveness of the method are verified through parameter analysis and comparative evaluation with existing approaches.