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<p>Multi-expert multi-criteria decision-making (MEMCDM) based on interval-set information is novel and valuable, and it already adopts an effective strategy of alternative queuing method (AQM), called AQM-IS. AQM-IS mainly relies on dissimilarity measures and possibility degrees of interval sets, and the two types of uncertainty measures have absolute-quantitative limitations on rough information extraction to imply improvement space. In this work, improved dissimilarity measures and possibility degrees of interval sets are constructed from a better perspective of relative quantization related to systematic structuring and statistical fusion, so improved AQM (called IAQM-IS) is established to advance MEMCDM by using the interval-set information transformation. As bases, relative dissimilarity measures are proposed to modify absolute dissimilarity measures for both interval-set pairs and families on closeness and deviation; thus, relevant internal relationships, mutual sizes, axiomatic properties, and illustrative examples are acquired. Aiming at interval-set information, improved AQM (i.e., IAQM-IS) is investigated for MEMCDM. Concretely, absolute dissimilarity measures are chosen to determine criterion weights based on judgement matrix and maximum deviation, and improved possibility degrees of interval sets are proposed by systematic likelihood characterizations and arithmetic mean combination; using the weight arithmetic mean of improved dissimilarity measures and possibility degrees, a more powerful index for sorting alternatives is generated to formulate IAQM-IS. For algorithmic evaluation, two assessment indices of decision rankings (called separability and goodness) are designed; accordingly, the two algorithms of MEMCDM — AQM-IS and IAQM-IS — are demonstrated and compared via both an applied examples of e-commerce platforms and six simulated experiments of public datasets, and thus the effectiveness and superiority of IAQM-IS are verified. In summary by the double-quantization technique, the improved dissimilarity measures and possibility degrees deepen uncertainty measures of interval-set information tables, and corresponding IAQM-IS has better decision performance than current AQM-IS in specific application scenarios of social cognition.</p>

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Improved Alternative Queuing Method of Interval-Set Dissimilarity Measures and Possibility Degrees for Multi-expert Multi-criteria Decision-Making

  • 期刊论文

  • 发表日期：

    2025年2月28日

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  • Xin Xie,
  • Xianyong Zhang,
  • Zhiying Lv,
  • Jiang Chen

摘要

Multi-expert multi-criteria decision-making (MEMCDM) based on interval-set information is novel and valuable, and it already adopts an effective strategy of alternative queuing method (AQM), called AQM-IS. AQM-IS mainly relies on dissimilarity measures and possibility degrees of interval sets, and the two types of uncertainty measures have absolute-quantitative limitations on rough information extraction to imply improvement space. In this work, improved dissimilarity measures and possibility degrees of interval sets are constructed from a better perspective of relative quantization related to systematic structuring and statistical fusion, so improved AQM (called IAQM-IS) is established to advance MEMCDM by using the interval-set information transformation. As bases, relative dissimilarity measures are proposed to modify absolute dissimilarity measures for both interval-set pairs and families on closeness and deviation; thus, relevant internal relationships, mutual sizes, axiomatic properties, and illustrative examples are acquired. Aiming at interval-set information, improved AQM (i.e., IAQM-IS) is investigated for MEMCDM. Concretely, absolute dissimilarity measures are chosen to determine criterion weights based on judgement matrix and maximum deviation, and improved possibility degrees of interval sets are proposed by systematic likelihood characterizations and arithmetic mean combination; using the weight arithmetic mean of improved dissimilarity measures and possibility degrees, a more powerful index for sorting alternatives is generated to formulate IAQM-IS. For algorithmic evaluation, two assessment indices of decision rankings (called separability and goodness) are designed; accordingly, the two algorithms of MEMCDM — AQM-IS and IAQM-IS — are demonstrated and compared via both an applied examples of e-commerce platforms and six simulated experiments of public datasets, and thus the effectiveness and superiority of IAQM-IS are verified. In summary by the double-quantization technique, the improved dissimilarity measures and possibility degrees deepen uncertainty measures of interval-set information tables, and corresponding IAQM-IS has better decision performance than current AQM-IS in specific application scenarios of social cognition.