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基于TOPSIS的动态三角模糊多属性决策方法

李美娟1,2, 易思成1, 邱启荣3, 林琦4   

  1. 1. 福州大学经济与管理学院, 福州 350108;
    2. 福建省社科研究基地福州大学福建经济高质量发展研究中心, 福州 350108;
    3. 福州大学科技处, 福州 350108;
    4. 莆田学院数学与金融学院, 莆田 351100
  • 收稿日期:2021-06-25 修回日期:2021-10-11 出版日期:2022-04-20 发布日期:2022-04-20
  • 通讯作者: 林琦,Email:Linqi1994@Yahoo.com.
  • 基金资助:
    国家自然科学基金项目(71872047),福建省百千万人才工程省级人选,福建省高校领军人才资助,福建省创新战略研究计划项目(2020R0021,2021R0077),福建省社科规划项目(FJ2020B024)资助课题.

李美娟, 易思成, 邱启荣, 林琦. 基于TOPSIS的动态三角模糊多属性决策方法[J]. 系统科学与数学, 2022, 42(3): 614-625.

LI Meijuan, YI Sicheng, QIU Qirong, LIN Qi. Dynamic Triangular Fuzzy Multi-Attribute Decision-Making Method Based on TOPSIS[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(3): 614-625.

Dynamic Triangular Fuzzy Multi-Attribute Decision-Making Method Based on TOPSIS

LI Meijuan1,2, YI Sicheng1, QIU Qirong3, LIN Qi4   

  1. 1. School of Economics and Management, Fuzhou University, Fuzhou 350108;
    2. Funding for Research Center of Fujian Economic High Quality Development Based on Social Science Planning of Fujian Province in China, Fuzhou 350108;
    3. Department of Science and Technology, Fuzhou University, Fuzhou 350108;
    4. School of Mathematics and Finance, Putian University, Putian 351100
  • Received:2021-06-25 Revised:2021-10-11 Online:2022-04-20 Published:2022-04-20
针对属性值为三角模糊数形式的动态多属性决策问题,提出基于TOPSIS的动态三角模糊多属性决策方法.该方法不仅可以得到各备选方案的差异程度,还可分析各备选方案的增长程度,同时设置偏好参数以考虑决策者对于差异性和增长性的不同偏好,运用时间权重进行二次加权以解决不同时点排序结果不一致的问题.最后,通过算例分析及偏好参数敏感性分析说明决策时考虑决策者偏好的必要性和重要性,并通过与其他方法的比较分析进一步验证了文章方法的可行性和有效性.
For the dynamic multi-attribute decision-making problem, in which the attribute values are in the form of triangular fuzzy numbers, a dynamic triangular fuzzy multi-attribute decision-making method based on TOPSIS is proposed. Through this method, not only the degree of difference of each alternative can be obtained, but also the degree of growth of each alternative can be analyzed. At the same time, the preference parameters are set to consider the different preferences of the decision-makers for the difference and growth, and the time weight is used for secondary weighting to solve the problem of inconsistent ranking results at different time points. Finally, the necessity and importance of considering the preference of decision-makers in decision-making are illustrated by example analysis and preference parameter sensitivity analysis, and the feasibility and effectiveness of this method are further verified by comparison with other methods.

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