对含有抽象属性的多属性层次结构而言，层次分析法即AHP（包括DIS-AHP、ABS-AHP、IDE-AHP和SUP-AHP四种具体方法）会因比率比较基准缺失、权重内涵模糊不清或方案评价不保序而缺乏科学理性。为发展AHP，基于摆幅置权（SW）判断模式和多属性决策属性价值公度方法，首先给出了能为层次结构抽象属性上的SW判断提供支持的规约性多属性决策属性价值公度方法，然后由此并结合多属性价值理论给出了能够克服现有层次分析法内在缺陷的目标导向层次分析方法即ToAHP。相对于AHP，ToAHP在判断模式与权重内涵、方法建构的理论基础和相关假设检验、方案评价保序与其内在数理依据上具有明显的相对科学合理性。应用分析表明：在输入信息可比的条件下，ToAHP明显优于AHP的四种分析方法之中最具可信性的SUP-AHP方法。
For multi-attribute hierarchies with non-specific attributes included, Saaty (1986,2006) has presented an approach of multiple attribute decision-making (MADM) to alternative evaluations/decisions, called the analytic hierarchy process (AHP), which has four specific types or application formats, namely AHP in the distributive mode (shorted as DIS-AHP), AHP in the absolute mode (ABS-AHP), AHP in the ideal mode (IDE-AHP) and AHP in the supermatrix mode (SUP-AHP). While AHP has widely been applied to real-world complex decisions, it, as well as its four specific types, has also suffered from a lot of academic criticisms, such as shortness of scientific rationality for lack of the reference-point of ratio comparisons, unclearness in the weights' meaning, and incapability of keeping alternative-evaluation ranking unchanged when an alternative is deleted from or a new alternative is added to the alternative set. To solve these shortcomings of AHP, three study efforts are made. First, based on the judgment mode of swing weighting (SW), and the approach to measure commensurable satisfaction values (CSV) of attribute performances in a MADM, a prescriptive approach to measure attribute-performances' CSVs in a MADM, called the prescriptive CSV approach, is presented to support the SW judgments on non-specific attributes in a multi-attribute hierarchy. The CSV of an alternative performance xi(xi ≥ xi,1) on the ith specific attribute is given by Mξi**(αi*)(xi-xi,1)αi*/?i**+MFi(xi|πi), where M denotes the number of target alternatives for reference, Fi(xi|πi) does the cumulative probability of xi relative to the attribute-performance distribution πi of target alternatives for reference in the ith specific attribute, xi,1 does the reference point given by the decision maker, and αi*,ξi**(αi*),?i** are parameters determined by a linear programming model. Second, based on the prescriptive CSV approach, a SW-like judgement mode for weights of attributes in every hierarchy level is presented. Third, based on the SW-like judgement mode for level-attributes' weights and the multiple-attribute value theory, a new approach to alternative evaluations/decisions with a multi-attribute hierarchy, called targets-oriented AHP (ToAHP), is presented. Compared with AHP, ToAHP has the following three advantages. Firstly, ToAHP can guarantee that attribute weights given by the decision maker are of clear meanings because of the adoption of SW judgments on every level attributes. Secondly, ToAHP is constructed on the robust basis of multiple attribute value theory, rather than simply on the primitive notions as AHP is, and thus whether or not the proposition of absolute preference independence adopted in ToAHP, as is also done in AHP, is applicable can be tested. Thirdly, ToAHP takes such an one-by-one procedure to evaluate alternatives that can not only keep the alternative ranking unchanged even if the alternative set is changed, but also is intrinsically of strict mathematical basis to guarantee rationally the unchanged alternative ranking. A case study shows that, on comparable conditions of input information, ToAHP is greatly superior to SUP-AHP, which is the most believable one among the four specific formats of AHP. For the mentioned-above reasons, ToANP can be considered as a better substitute of AHP when AHP is required to solve real-world complex decisions.
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