针对仿真结果动态一致性的检验,将距离检验方法和TIC(Theil s inequality coefficient)方法进行了对比分析.距离检验方法可以在一定显著性水平下给出一致性检验的拒绝域,在时域内实现了定量检验.TIC方法的检验指标是TIC系数,经过研究发...针对仿真结果动态一致性的检验,将距离检验方法和TIC(Theil s inequality coefficient)方法进行了对比分析.距离检验方法可以在一定显著性水平下给出一致性检验的拒绝域,在时域内实现了定量检验.TIC方法的检验指标是TIC系数,经过研究发现,TIC系数的计算结果可以通过坐标原点的选取而进行人为调整,大大增加了误判的可能性,所以TIC方法既不能定量检验,也不能有效的定性检验.对比研究表明:距离检验方法不受量纲、坐标原点选取等因素影响,其检验结果具有更高的可信性.展开更多
This paper introduces uncertainty theory to deal with non-deterministic factors in ranking alternatives. The uncertain variable method (UVM) and the definition of consistency for uncertainty comparison matrices are pr...This paper introduces uncertainty theory to deal with non-deterministic factors in ranking alternatives. The uncertain variable method (UVM) and the definition of consistency for uncertainty comparison matrices are proposed. A simple yet pragmatic approach for testing whether or not an uncertainty comparison matrix is consistent is put forward. In cases where an uncertainty comparison matrix is inconsistent, an algorithm is used to generate consistent matrix. And then the consistent uncertainty comparison matrix can derive the uncertainty weights. The final ranking is given by uncertainty weighs if they are acceptable;otherwise we rely on the ranks of expected values of uncertainty weights instead. Three numerical examples including a hierarchical (AHP) decision problem are examined to illustrate the validity and practicality of the proposed methods.展开更多
文摘针对仿真结果动态一致性的检验,将距离检验方法和TIC(Theil s inequality coefficient)方法进行了对比分析.距离检验方法可以在一定显著性水平下给出一致性检验的拒绝域,在时域内实现了定量检验.TIC方法的检验指标是TIC系数,经过研究发现,TIC系数的计算结果可以通过坐标原点的选取而进行人为调整,大大增加了误判的可能性,所以TIC方法既不能定量检验,也不能有效的定性检验.对比研究表明:距离检验方法不受量纲、坐标原点选取等因素影响,其检验结果具有更高的可信性.
文摘This paper introduces uncertainty theory to deal with non-deterministic factors in ranking alternatives. The uncertain variable method (UVM) and the definition of consistency for uncertainty comparison matrices are proposed. A simple yet pragmatic approach for testing whether or not an uncertainty comparison matrix is consistent is put forward. In cases where an uncertainty comparison matrix is inconsistent, an algorithm is used to generate consistent matrix. And then the consistent uncertainty comparison matrix can derive the uncertainty weights. The final ranking is given by uncertainty weighs if they are acceptable;otherwise we rely on the ranks of expected values of uncertainty weights instead. Three numerical examples including a hierarchical (AHP) decision problem are examined to illustrate the validity and practicality of the proposed methods.
