混合信息下的多属性大规模群体决策方法

doi:10.3969/j.issn.1001-506X.2020.11.19

摘要/Abstract

摘要：

针对多属性大规模群体决策问题中决策者评价信息类型不一致的情况,提出一种包含精确数、区间数、语言信息的多属性大规模群体决策方法。首先,定义了几种不同的转换函数,以此将各决策者给出的精确数、区间数、语言信息一致化为虚拟语言术语;然后,给出了大规模群体决策矩阵的的分组方法,并利用新的三维语言密度算子对其进行集结;最后,在语言幂Heronian算子的基础上,对群体综合决策矩阵进行集结。通过算例验证了所提方法的可行性与有效性。算例分析表明,该方法不仅能够较好地解决混合信息的处理问题,以及能够充分考虑群体共识度;还能够体现属性之间的关联性,以及反映集结值的整体均衡性,因此可使得决策结果更为客观合理。

关键词: 混合信息, 大规模, 多属性群决策, 三维语言密度算子, 语言幂Heronian算子

Abstract:

The evaluation information types of decision makers are inconsistent in the multiple attribute large-scale group decision making problem. To overcome this kind of defects, a multiple attribute large-scale group decision making method based on hybrid information is proposed. Firstly, several different conversion functions are defined to transform the hybrid evaluation information that contains the precise number, interval number and linguistic information into virtual linguistic terms. Secondly, the proper cluster is made for the large-scale group evaluation matrix, and it is aggregated by the three-dimensional linguistic density operator. Finally, the linguistic power Heronian operator is given to aggregate the group comprehensive evaluation matrix. A numerical example is provided to prove the feasibility and validity of the proposed method. The results show that this method can not only solve the problem of inconsistent hybrid information and consider the group consensus degree, but also reflect the correlation among attributes and the overall balance of the aggregated value.

Key words: hybrid information, large-scale, multiple attribute group decision making, three-dimensional linguistic density operator, linguistic power Heronian operator

中图分类号:

C931

王伟明, 徐海燕. 混合信息下的多属性大规模群体决策方法[J]. 系统工程与电子技术, 2020, 42(11): 2560-2569.

Weiming WANG, Haiyan XU. Multiple attribute large-scale group decision making method based on hybrid information[J]. Systems Engineering and Electronics, 2020, 42(11): 2560-2569.

图/表 2

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方案	本文方法		方法一		方法二
方案	语言评价值	排序	直觉模糊评价值	排序	精确评价值	排序
O₁	s_{2.660 1}	5	0.265 8	5	2.654 2	5
O₂	s_{3.415 9}	4	0.356 4	3	3.404 8	4
O₃	s_{4.579 9}	2	0.451 2	2	4.574 9	1
O₄	s_{3.493 4}	3	0.333 3	4	3.489 2	3
O₅	s_{4.626 6}	1	0.457 7	1	4.564 5	2

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