基于虚拟最劣解TOPSIS和灰关联度的动态评价方法

徐林明,李美娟,欧忠辉,谢雅萍

系统科学与数学 ›› 2019, Vol. 39 ›› Issue (3) : 365-377.

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PDF(596 KB)
系统科学与数学 ›› 2019, Vol. 39 ›› Issue (3) : 365-377. DOI: 10.12341/jssms13599
论文

基于虚拟最劣解TOPSIS和灰关联度的动态评价方法

    徐林明1,李美娟2,欧忠辉2,谢雅萍2
作者信息 +

Dynamic Evaluation Method Based on Virtual Worst Solution TOPSIS and Gray Correlation Degree

    XU Linming1 ,LI Meijuan2 ,OU Zhonghui2 ,XIE Yaping2
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文章历史 +

摘要

为了比较多个系统在不同时刻的发展水平和某一段时间的动态累积水平, 需要进 行动态评价.针对传统TOPSIS和灰关联度法优缺点, 提出新贴近度, 同时反映了方案与理想方案和虚拟最劣解之间的位置关系以及方案和理想方案数据曲线的相似性差异.引入虚拟最劣解对传统TOPSIS进行改进, 将二维数据加入时序数据扩展为三维数据, 提出基于虚拟最劣解TOPSIS和 灰关联度的动态评价方法, 该方法既可以得到反映各评价对象指标值差异程度的评价值及排序结果, 也可以得到各评价对象增长程度的评价值及排序结果, 还可以得到同时考虑各评价对象指标值差异程度和增长程度的综合评价值.根据决策者对位置和形状的偏好程度以及存量和增量偏好程度不同, 对相应参数 取不同值.既可以得到各评价对象各时刻的综合评价值及排序, 也可以得到各评价对象在某个时间段内总体的评价值和排序结果.最后将该方法应用于``十二五''期间区域协同创新能力评价, 通过实例验证 该方法实际应用上的有效性.

Abstract

In order to compare the development level of multiple systems at different times and the dynamic accumulation level for a certain period of time, dynamic evaluation is required. Aiming at the advantages and disadvantages of traditional TOPSIS and grey correlation method, a new closeness is proposed. At the same time, it reflects the positional relationship between the alternative and the ideal solution and the virtual worst solution, and the data curve similarity difference between the alternative and the ideal solution. Introducing virtual worst solution to improve traditional TOPSIS, add time series data to the two-dimensional data and expand it into three-dimensional data, a dynamic evaluation method based on virtual worst solution TOPSIS and gray relational degree is proposed. The method can not only obtain the evaluation value and the ranking result that reflect the difference degree of each target value of the evaluation object, but also can obtain the evaluation value and ranking result of the growth degree of each evaluation object. It is also possible to obtain a comprehensive evaluation value that simultaneously considers the degree of difference and the degree of growth of each evaluation object index value. According to the preference of the decision maker, we take different values for the corresponding parameters. α reflects the preference of decision makers for position and shape, and β reflects the preference of decision makers for differences in index values and growth of index values. The method can not only obtain the comprehensive evaluation value and ranking of each evaluation object at each time, but also can obtain the overall evaluation value and ranking result of each evaluation object within a certain time period. Finally, this method will be applied to the evaluation of regional collaborative innovation capability during the 12th Five-Year Plan period. The validity of the practical application of the method is verified by an example.

关键词

理想解 /   / 灰色关联度 /   / 动态评价方法 /   / 三维数据 /   / 区域协同创新能力.

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徐林明 , 李美娟 , 欧忠辉 , 谢雅萍. 基于虚拟最劣解TOPSIS和灰关联度的动态评价方法. 系统科学与数学, 2019, 39(3): 365-377. https://doi.org/10.12341/jssms13599
XU Linming , LI Meijuan , OU Zhonghui , XIE Yaping. Dynamic Evaluation Method Based on Virtual Worst Solution TOPSIS and Gray Correlation Degree. Journal of Systems Science and Mathematical Sciences, 2019, 39(3): 365-377 https://doi.org/10.12341/jssms13599
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