计算机工程与应用 ›› 2017, Vol. 53 ›› Issue (3): 47-53.DOI: 10.3778/j.issn.1002-8331.1505-0277

• 理论与研发 • 上一篇    下一篇

2TLCGPOWA算子及其在多属性群决策中的应用

吴  群1,吴  澎1,周元元1,周礼刚1,2,陈华友1   

  1. 1.安徽大学 数学科学学院,合肥 230601
    2.南加州大学 电气工程系 信号与图像处理实验室,加利福尼亚州 洛杉矶 90089
  • 出版日期:2017-02-01 发布日期:2017-05-11

Generalized 2-Tuple Linguistic Connection Power Ordered Weighted Average operator and its application to multiple attribute group decision making

WU Qun1, WU Peng1, ZHOU Yuanyuan1, ZHOU Ligang1, 2, CHEN Huayou1   

  1. 1.School of Mathematical Sciences, Anhui University, Hefei 230601, China
    2.Signal of Image Processing Institute, Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA
  • Online:2017-02-01 Published:2017-05-11

摘要: 基于集对分析理论中二元联系数的不确定性,将联系变量引入到区间二元语义环境中,定义了二元语义联系变量,给出了二元语义联系变量的运算法则,并提出了几种新的算术集结算子。针对决策矩阵元素为区间二元语义变量和属性权重完全未知的不确定多属性群决策问题,提出了一种基于2TLCGPOWA算子的不确定多属性群决策方法。最后通过对某大学教师的任职和晋升考核来说明该方法的可行性和有效性。

关键词: 多属性群决策, 二元语义联系变量, 广义二元语义联系power有序加权平均(2TLCGPOWA)算子, 评价

Abstract:  Based on the uncertainty of binary connection number in the set pair analysis, connection variables are extended to interval 2-tuple linguistic environment, and the 2-tuple linguistic connection variables are defined. Moreover, some operational laws of 2-tuple linguistic connection variables are proposed and several new arithmetic aggregation operators are developed. For the multiple attributes decision making problem in which the attribute values are in the form of interval 2-tuple linguistic variables and the weights of attributes are unknown, an approach to applying the 2TLCGPOWA operator to uncertain multiple attribute group decision making is developed. Finally, a numerical example of tenure and promotion evaluation of teachers of a university is given to illustrate the feasibility and effectiveness of the new approach.

Key words: multiple attribute group decision making, 2-tuple linguistic connection variables, Generalized 2-Tuple Linguistic Connection Power Ordered Weighted Average(2TLCGPOWA) operator, evaluation