区间直觉模糊熵–集对分析–理想解耦合的多属性决策模型
Multi-attribute decision making model coupled with interval valued intuitionistic fuzzy entropy-set pair analysis-technique for order preference by similarity to an ideal solution
摘要点击 30  全文点击 41  投稿时间:2018-08-30  修订日期:2019-07-19
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DOI编号  10.7641/CTA.2019.80654
  2020,37(1):176-186
中文关键词  区间直觉模糊熵  集对分析  博弈论  灰色关联度  逼近理想解排序法
英文关键词  interval valued intuitionistic fuzzy entropy  set pair analysis  game theory  grey correlation theory  TOPSIS
基金项目  
学科分类代码  
作者单位E-mail
任红岗 北京矿冶科技集团有限公司 15120088723@163.com 
谭卓英 北京科技大学  
中文摘要
      属性决策中指标集具有关联度多样性、区间模糊性等特点,传统决策模型在这些方面还没有很好的解决方法。为此,本文提出区间直觉模糊熵-集对分析-理想解耦合的综合评价模型,基于区间模糊熵和集对分析理论,运用对立与统一的观点对评价集的确定性和不确定性因素进行系统分析,量化了评价集同、异、反三个角度之间的关联度,引入博弈论对权重确定方法进行了优化,兼顾了主观权重和客观权重。研究表明,该决策模型充分考虑了比较集的同一、对立、差异度的区间性,全面吸纳了不同决策者的评估信息,既反映了客观信息,又反映了决策者的主观意愿,为多属性决策提供了新的方法。
英文摘要
      The index set of multi-attribute decision-making has the characteristics of diversity of correlation degree and interval fuzziness., traditional decision-making models have not been well solved in these aspects. Therefore, A comprehensive evaluation model coupled with interval valued intuitionistic fuzzy entropy-set pair analysis-technique for order preference by similarity to an ideal solution is proposed in this paper. Basing on the theory of interval fuzziness entropy and set pair analysis, and with a dialectical view of unity of opposites on certainty and uncertainty factors of the evaluation set, the model has quantified correlation degree among the same, different and inverse angles of evaluation set at the first place. In addition, the model has introduced the game theory to optimize the weight determination method, considering both subjective and objective weights. The results show that the decision-making model fully considers the interval of the same, opposite and difference degree of the Fuzzy Uncertain comparative set, and absorbs the evaluation information of different decision-makers. It reflects not only objective information but also subjective wishes of decision-makers. It provides a new method for multi-attribute decision-making.