基于广义灰色激励因子的多源不确定性指标动态综合评价模型研究

由于多数评价指标的数据来源多、存储方式不一、处理方法各异等,使得其具有多源不确定性(multi-source uncertainty, MSU)特点,具体表现为指标评价值往往包括灰数、模糊数、区间值模糊数以及白数等。针对此类指标的评价问题,提出了新的基于广义灰色激励因子的多源不确定性指标(multi-source uncertain index, MSUI)动态综合评价模型,首先引入广义灰数的基本理念,将传统的灰色绝对关联模型扩展到评价指标为广义灰数的形式,由此得出各个时间段指标的广义灰色激励因子;然后根据指标信息不对称的特点,构建基于MSUI的广义灰色熵权模型,求解出各个指标的广义灰色熵权;接着在广义灰色激励因子与广义灰色熵权的基础上,建立基于广义灰色激励因子的MSUI动态综合评价模型;最后结合案例研究证明了模型的有效性与可行性。

Research on the dynamic comprehensive evaluation model of multi-source uncertain indexes based on the generalized grey incentive factors

Most of the evaluation indexes have the characteristics of multi-source uncertainty (MSU) because the data of them have much source, different storage mode and handling methods. It is reflected by the evaluation values including grey number, fuzzy number, interval-valued fuzzy number and white number. For the evaluation problem of such indexes, we propose a new dynamic comprehensive evaluation model of multi-source uncertain index (MSUI) based on the generalized grey incentive factors. At first, we introduce the generalized grey number and extend the traditional grey absolute correlation model to the form of evaluation indexes of generalized grey number, thus determine the generalized grey incentive factors. Then according to the characteristic of information asymmetry of indexes, we build the generalized grey entropy weight model based on the MSUI, and determine the generalized grey entropy weight of each index. Next, we build the dynamic comprehensive evaluation model of MSUI based on the generalized grey incentive factors with the generalized grey incentive factors and the generalized grey entropy weight. Finally, it proves the effectiveness and feasibility of the model through the case study.