目标导向序依赖层次分析法

建构在多属性决策序依赖Choquet积分模型之上的层次化多属性评价与决策方法(简称作TOYLC层次分析法),并没有保证Choquet积分模型所要求的价值测度公度性.另外,它依赖MACBETH (measuring attractiveness by a categorical-based evaluation technique)所给出的属性集容量判断方法,也存在着对抽象方案进行偏好比较的技术缺陷.针对上述问题,在引入规约性多属性决策属性价值公度方法的基础上,首先给出了类似于摆幅置权判断、能够使决策者进行有意义偏好比较的属性集容量判断赋值方法,然后给出了能够克服TOYLC层次分析法内在缺陷的目标导向序依赖层次分析法.基于案例应用的对比分析表明:在输入信息可比的条件下,目标导向序依赖层次分析法相对于TOYLC层次分析法具有更高的方案评价区分度,从而验证了前者相对于后者的相对科学合理性.

Targets-oriented analytic hierarchy process for ordinal preference dependence

There are two problems within the approach built on Choquet integral model (CIM) for hierarchical multiple attribute decision-making (MADM), called TOYLC analytic hierarchy process (shorted as TOYLC-AHP). First, it does not in effect guarantee the satisfaction commensurability that CIM requires. Second, the method that is built on the measuring attractiveness by a categorical-based evaluation technique and that TOYLC-AHP adopts to determine the capacities of attribute sets, also suffers from making judgements on non-specific alternatives. To develop TOYLC-AHP, the prescriptive commensurable method (PCM) for alternatives' satisfaction values on multiple attributes, is proposed to measure the commensurable satisfaction values of alternatives' performances on the specific attributes in a hierarchy. Based on PCM, a judgement mode for determining attribute-set capacities, similar to the swing weighting mode widely used in simple MADMs, is firstly presented to help dicision-makers make meaningful preference comparisons. Then, based on the new judgement mode and PCM, a new approach called targets-oriented AHP for ordinal preference dependence (ToAHPOPD) is given to substitute for TOYLC-AHP. A case study shows that ToAHPOPD is able to make better discriminations on alternatives than TOYLC-AHP, and thus verified to be superior to TOYLC-AHP.