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<p>In multi-attribute group decision-making (MAGDM), q-rung orthopair fuzzy sets (q-ROFSs) provide a flexible framework for representing uncertain information. The reliability of MAGDM models under q-ROFSs largely depends on information measures, which directly affect weight determination, information aggregation, and alternative ranking. However, existing information measures for q-ROFSs still have notable limitations. Many entropy measures ignore hesitation information and may suffer from division-by-zero. Some score functions may yield counter-intuitive results and exhibit limited discrimination ability. These issues indicate the need for more reliable information measures and decision-making methods under q-ROFSs. Motivated by these limitations, this paper develops novel information measures of q-ROFSs and further develops an extended MAGDM method. Specifically, we establish a new axiomatic framework for uncertainty in q-ROFSs and propose an uncertainty measure that simultaneously captures hesitation and fuzziness. The proposed uncertainty measure is embedded into an optimization model to derive attribute weights. In addition, we propose a novel score function that considers hesitation and offers improved discrimination ability. The score function is integrated with the Criteria Importance Through Intercriteria Correlation (CRITIC) method to determine expert weights. Finally, an extended Weighted Aggregated Sum Product Assessment (WASPAS) method is adopted to rank alternatives. A case study on food waste treatment technology (FWTT) selection identifies anaerobic digestion as the optimal alternative. Comparison with eleven existing MAGDM methods demonstrates the effectiveness and reliability of the proposed method. Sensitivity analysis and dataset experiments further confirm its robustness and practical applicability.</p>

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An extended multi-attribute group decision-making method based on novel information measures and its application

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Weishu Peng,
  • Chunfang Chen,
  • Jing Tang,
  • Xiangjun Li,
  • Xuan Huang

摘要

In multi-attribute group decision-making (MAGDM), q-rung orthopair fuzzy sets (q-ROFSs) provide a flexible framework for representing uncertain information. The reliability of MAGDM models under q-ROFSs largely depends on information measures, which directly affect weight determination, information aggregation, and alternative ranking. However, existing information measures for q-ROFSs still have notable limitations. Many entropy measures ignore hesitation information and may suffer from division-by-zero. Some score functions may yield counter-intuitive results and exhibit limited discrimination ability. These issues indicate the need for more reliable information measures and decision-making methods under q-ROFSs. Motivated by these limitations, this paper develops novel information measures of q-ROFSs and further develops an extended MAGDM method. Specifically, we establish a new axiomatic framework for uncertainty in q-ROFSs and propose an uncertainty measure that simultaneously captures hesitation and fuzziness. The proposed uncertainty measure is embedded into an optimization model to derive attribute weights. In addition, we propose a novel score function that considers hesitation and offers improved discrimination ability. The score function is integrated with the Criteria Importance Through Intercriteria Correlation (CRITIC) method to determine expert weights. Finally, an extended Weighted Aggregated Sum Product Assessment (WASPAS) method is adopted to rank alternatives. A case study on food waste treatment technology (FWTT) selection identifies anaerobic digestion as the optimal alternative. Comparison with eleven existing MAGDM methods demonstrates the effectiveness and reliability of the proposed method. Sensitivity analysis and dataset experiments further confirm its robustness and practical applicability.