检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
机构地区:[1]辽宁工程技术大学公共管理与法学院,阜新123000 [2]辽宁工程技术大学工商管理学院,阜新123000
出 处:《应用泛函分析学报》2015年第4期400-406,共7页Acta Analysis Functionalis Applicata
基 金:国家自然科学基金(71071113);中国博士后科学基金(2012M520937)
摘 要:在多准则决策中,指标间完全独立是少见的,指标相关却是一种常态.指标对评价目标直接"贡献"和该指标通过其他指标对评价目标产生的间接"贡献"是确定关联指标权重的关键.本文利用经济学中的"弹性系数"对指标"贡献"进行测度.根据确定独立指标权重的方法可获得指标直接弹性系数;通过绘制指标间的因果关系图,构造指标弹性系数矩阵,通过矩阵运算可得到各指标的间接弹性系数.最后根据各指标总的弹性系数计算指标权重.结果显示,指标权重是该指标弹性系数占所有指标弹性系数总和的比例,独立指标权重是关联指标权重的特例.实例表明,在指标关联条件下,忽视指标关联得到的权重存在较大误差,具有重要影响作用的指标权重会被低估.In multi-criteria decision making, full independence is rare among indica- tors, but it is a norm that indicators are related. The key to determine the related indicators weights is the direct "contributions" that the indicators work on the target and the indirect "contributions" that the indicators work on the target through other indicators. In this paper, it uses economics "elasticity coefficient" to measure indi- cators "contributions". It can get index direct elasticity coefficient by using the way of determining independent index weights; It can construct index elasticity coefficient matrix by drawing a causal relationship diagram among indicators, and can get index elasticity coefficient among indicators through matrix operations. Finally, it calculates the index weights according to every indicator's total elasticity coefficient. The results show that the index weight is the proportion of the index elasticity coefficient in the sum of index elasticity coefficient, the independent index weight is a special case of related index weight. The example shows that, under the condition of related indica- tors, to get index weght by ignoring related indicators will cause huge errors, the index weights that the indicator have an important influence will be underestimated.
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.38