首页> 外文期刊>International journal of entelligent systems >Multiple attribute decision-making method for dealing with heterogeneous relationship among attributes and unknown attribute weight information under q-rung orthopair fuzzy environment
【24h】

Multiple attribute decision-making method for dealing with heterogeneous relationship among attributes and unknown attribute weight information under q-rung orthopair fuzzy environment

机译:q-阶邻态对模糊环境下处理属性与未知属性权重信息异类关系的多属性决策方法

获取原文
获取原文并翻译 | 示例
           

摘要

A Q-rung orthopair fuzzy set (q-ROFS) originally proposed by Yager (2017) is a new generalization of orthopair fuzzy sets, which has a larger representation space of acceptable membership grades and gives decision makers more flexibility to express their real preferences. In this paper, for multiple attribute decision-making problems with q-rung orthopair fuzzy information, we propose a new method for dealing with heterogeneous relationship among attributes and unknown attribute weight information. First, we present two novel q-rung orthopair fuzzy extended Bonferroni mean (q-ROFEBM) operator and its weighted form (q-ROFEWEBM). A comparative example is provided to illustrate the advantages of the new operators, that is, they can effectively model the heterogeneous relationship among attributes. We prove that some existing known intuitionistic fuzzy aggregation operators and Pythagorean fuzzy aggregation operators are special cases of the proposed q-ROFEBM and q-ROFEWEBM operators. Meanwhile, several desirable properties are also investigated. Then, a new knowledge-based entropy measure for q-ROFSs is also proposed to obtain the attribute weights. Based on the proposed q-ROFWEBM and the new entropy measure, a new method is developed to solve multiple attribute decision making problems with q-ROFSs. Finally, an illustrative example is given to demonstrate the application process of the proposed method, and a comparison analysis with other existing representative methods is also conducted to show its validity and superiority.
机译:Yager(2017)最初提出的Q阶邻对模糊集(q-ROFS)是邻对模糊集的一种新概括,具有可接受的会员等级的较大表示空间,并为决策者提供了更大的灵活性来表达他们的真实偏好。针对带有q-级邻态对模糊信息的多属性决策问题,提出了一种处理属性与未知属性权重信息异类关系的新方法。首先,我们介绍了两个新颖的q-阶邻对模糊模糊Bonferroni均值(q-ROFEBM)算子及其加权形式(q-ROFEWEBM)。提供了一个比较示例来说明新运算符的优点,也就是说,它们可以有效地建模属性之间的异构关系。我们证明,一些现有的已知直觉模糊聚合算子和毕达哥拉斯模糊聚合算子是拟议q-ROFEBM和q-ROFEWEBM算子的特例。同时,还研究了几种理想的性能。然后,提出了一种新的基于知识的q-ROFSs熵测度,以获取属性权重。基于提出的q-ROFWEBM和新的熵测度,开发了一种解决q-ROFSs多属性决策问题的新方法。最后,给出一个实例说明该方法的应用过程,并与其他现有的代表性方法进行比较分析,以证明其有效性和优越性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号