属性集容量确定的夹挤式测度模式及其推算模型

迄今,为解决多属性偏好关联决策属性集容量判断指数复杂性难题所提出的λ模糊测度模式与k-可加模糊测度模式,以及建构在它们之上的属性集容量确定的推算模型,尚存在着适用性差的技术不足。为此,以平衡容量判断的可操作性和容量推算的准确性为视角,提出了一种新容量测度模式,即关于容量判断与推算的夹挤式测度模式,并在此基础上通过引入决策者较易判断给出的容量序信息构建了相应的容量推算模型。基于数值模拟的对比分析表明：新模式不仅在应用可行性上高于k-可加模糊测度模式,而且从容量推算的准确性上看也明显优于λ模糊测度模式和k-可加模糊测度模式,因而对实际决策具有更强的适用性。

Sandwich Measurement Pattern on Attribute-set Capacity Determination and Its Corresponding Capacity Calculation Model

For multiple attribute decision making with preference dependence relationship (MADMPDR), a problem is that the decision maker is required to estimate too many attribute-set capacities, which is also called the exponential complexity problem of attribute-set capacity determination. For this problem, the two fuzzy measures namely the λ-measure and the k-order measure, as well as attribute-set capacity calculation (ASCC) models based on these two measures, have been presented in literature. However, they are suffered from impracticality in many decision cases due to arbitrary hypotheses on preference dependence relationship. The impracticality embodies in two aspects. One is the unfeasibility of measurement pattern, the other is the inaccurateness in capacity calculation. To overcome the impracticality of the λ-measure and the k-order measure, a new measurement pattern on attribute-set capacity, called sandwich measurement pattern (SMP), is proposed based on such a strategy of balancing the feasibility of attribute-set capacity determination with the accurateness of ASCC. Then, a linear programming model for ASCC corresponding to SMP, shorted as LPM-SMP, is presented. In specific, the model determines an attribute-set capacity through restricting it between the minimum and maximum of attribute-set capacities of the same order, and squeezing it into a particular range by theoretical quantitative relationships of attribute-set capacities. These relationships include μ(Ø)=0, μ(N)=1, and μ(A∪{C_i})-μ(A) ≥ 0, where N represents the attribute set of attributes C₁,…,C_n, μ(·) does the capacity of the attribute-set denoted by "·", {C_i} does the singleton of attribute C_i, and A does a subset of N except {C_i}. Besides, the attribute-set capacity given by LPM-SMP satisfies other constraints built on the judgment information of the decision maker. The judgment information is of three types. The first is on the attribute-set capacity ranks, the second is on the numerical values of low-order attribute-set capacities, and the third is on the minimum and maximum attribute-set capacities within a high-order capacity rank. SMP as well as LPM-SMP has two advantages. First, the number of attribute-set capacities to be estimated is C_n¹+C_n²+2(n-3) for a decision with n attributes, which is very acceptable for decision makers since it is only approximate to that when the 2-order measure is adopted. Second, the given attribute-set capacities are more rational than those given by ASCC models based on the λ-measure and k-order measure, since arbitrary hypotheses like those adopted in the measures mentioned above are no longer made in SMP. Data simulation analysis shows that SMP is not only more feasible than the k-order measure, but also greatly superior in calculation accurateness to the λ-measure and the k-order measure. As a result, SMP is more applicable to real-world decisions of MADMPDR.