不确定语言信息下的大规模群体DEMATEL决策方法
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南京航空航天大学 经济与管理学院,南京 211106

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E-mail: wwmlsy@163.com.

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C934

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国家自然科学基金项目(71971115,71701097,61673209);江苏省自然科学基金项目(BK20170777).


Large-scale group DEMATEL decision making method under uncertain linguistic information
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School of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

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    摘要:

    针对现有不确定语言决策试验和评价实验(DEMATEL)方法大多仅适用于中小规模群体,且未考虑不确定语言变量采用非均匀标度的情况,探讨一种新的不确定语言信息下的大规模群体DEMATEL决策方法.首先,给出一种群体不确定语言DEMATEL矩阵的聚类方法,并对大规模群体DEMATEL意见进行合理分组;然后,定义三维不确定语言密度算子,并对大规模群体DEMATEL意见进行有效集结;最后,提出一种改进的基于不确定语言DEMATEL的指标权重确定方法,并在此基础上进行多属性决策.算例分析验证了所提方法的可行性与有效性,该方法采用非均匀标度来表征和呈现不确定语言变量,而且考虑了大规模群体决策的共识度,使得决策结果更为客观合理且更为可靠.

    Abstract:

    So far the decision making trial and evaluation laboratory(DEMATEL) decision making methods based on uncertain linguistic information are more suitable for small and medium-sized groups, and do not consider that uncertain linguistic variables use the non-uniform evaluation scale. To overcome this kind of defects, a new large-scale group DEMATEL decision making method under uncertain linguistic information is proposed. Firstly, a reasonable cluster method for the uncertain linguistic group DEMATEL matrix is given, which is used to make a cluster for large-scale group DEMATEL evaluation information. Then, the three dimensional uncertain linguistic density operator is defined, which is used to aggregate large-scale group DEMATEL evaluation information. Finally, an improved index weights calculation method based uncertain linguistic DEMATEL is proposed, on this basis, the multiple attribute decision making is implement. One numeral example is used to illustrate the feasibility and effectiveness of the proposed method. The results show that the decision making is rational and credible as it can use the uncertain linguistic variables with non-uniform evaluation scale and consider large-scale group decision making's consensus degree.

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王伟明,徐海燕.不确定语言信息下的大规模群体DEMATEL决策方法[J].控制与决策,2021,36(8):2023-2033

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  • 在线发布日期: 2021-07-13
  • 出版日期: 2021-08-20
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