基于方向距离函数和信息熵的集成交叉效率模型

Integrated cross-efficiency model based on directional distance function and information entropy

针对传统交叉效率模型无法处理输入输出数据中同时含有负数的问题,提出一种基于方向距离函数和信息熵的集成交叉效率模型。首先,利用方向距离函数的思想对负数进行处理。其次,结合交叉效率模型实现决策单元的完全排序。接着,借助信息熵的变异系数获取一组用于交叉效率集结的公共权重,以避免传统模型的权重偏差,且保留评价过程中的决策信息。最后,通过一个数值案例验证了本研究模型的有效性和实用性,扩展了交叉效率模型的研究范围和应用场景。

Aiming at the problem that traditional cross-efficiency model cannot handle both input and output data containing negative numbers,an integrated cross-efficiency model based on directional distance function and information entropy was proposesed.First of all,the idea of direction distance function was used to deal with negative numbers.Secondly,the complete ranking of decision units was realized by combining cross efficiency.Then,with the help of the variation coefficient of information entropy,a set of public weights for cross-efficiency integration were obtained to avoid the weight deviation of the traditional model and retain the decision information in the evaluation process.Finally,the effectiveness and practicability of the proposed model were verified by a numerical example,and the research scope and application scenarios of the crossefficiency model were extended.