Abstract

The multiplier network directional distance function (DDF) model capable of handling negative data received little attention in the field of data envelopment analysis (DEA). The series system is a basic network one. Under the assumption of variable returns to scale (VRS), this paper extends the multiplier series DEA model for use with the DDF. The proposed series DDF model is non-oriented and can deal with negative data. The resulting system efficiency score can be decomposed as a weighted average of process efficiency scores. In the context of DDF, the issue of possible alternate process efficiencies is addressed. The proposed model can also be applied to the assumption of constant returns to scale (CRS). Moreover, we derive the mathematical relationship between the CRS form of our series DDF model and the series CCR model. Two empirical examples in the literature illustrate the applicability and advantages of the new model.

摘要

能够处理负数据的乘数网络定向距离函数（DDF）模型在数据包络分析（DEA）领域很少受到关注。系列系统是基础网络系统。在规模收益可变（VRS）的假设下，本文扩展了乘数级数 DEA 模型以与 DDF 一起使用。所提出的序列 DDF 模型是无向的，可以处理负数据。得到的系统效率得分可以分解为过程效率得分的加权平均值。在 DDF 的背景下，解决了可能的替代流程效率问题。所提出的模型也可以应用于规模报酬不变（CRS）的假设。此外，我们推导了我们的系列 DDF 模型的 CRS 形式和系列 CCR 模型之间的数学关系。