基于二元语义的语言加权取大改进算法的研究  

Improved Linguistic Weighted Maximum Operator Based on Two-Tuple Linguistic Information Processing

作　　者：鲍广宇[1] 连向磊[1] 何明[1] 肖登海[1] 

机构地区：[1]解放军理工大学指挥自动化学院,江苏南京210007

出　　处：《计算机技术与发展》2009年第11期49-52,共4页Computer Technology and Development

基　　金：总装预研基金项目(9140A06020206JB8102)

摘　　要：为了求解基于自然语言评价信息的多属性群决策问题,提出了一种基于二元语义的语言加权取大(T-LWM)改进算法。该算法利用二元语义对传统语言加权取大算法进行改进,将语言评价信息转换成二元语义形式,求取各决策者给出的属性权重平均值作为方案集结数据。该方法的目标是降低决策结果易受个别决策者不良数据影响,提高算法的健壮性。验证结果表明:与传统的语言加权取大算法相比,该算法具有运算简便,决策过程客观,分辨方案能力强的优点。A new method which based on two - tupte linguistic is proposed to solve the deficiency of the traditional linguistic weighted maximum operator in solving multi- attribute group decision making problems. In the method, the linguistic assessment information is transformed into the 2 - tuph linguistic representation, the average of different attributes weight value is taken into account. The object of the method is that amend the shortage of which the result is likely to be impacted by the ＂bad＂ value, make the algorithm robust. Finally, an example is used to demonstrate the good character of the improved operator： convenient, impersonal, particular.

关 键 词：群体决策 偏好集结 二元语义 加权取大 

分 类 号：C934[经济管理—管理学] N945.25[自然科学总论—系统科学]