Decomposition weights and overall efficiency in two-stage additive network DEA
Decomposition weights and overall efficiency in two-stage additive network DEA
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DOI：
10.1016/j.ejor.2016.08.002
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发表时间：
2017-03-16
影响因子：
6.4
通讯作者：
Zhu, Joe
中科院分区：
文献类型：
--
作者：
Guo, Chuanyin;Shureshjani, Roohollah Abbasi;Zhu, Joe
数据包络分析（DEA）是一种同行决策单元绩效评价技术。网络DEA模型研究dmu的内部结构。本文以两阶段网络结构为例，研究了加性效率分解，其中总效率被定义为阶段效率的加权平均值，并用权重来反映各个阶段的相对重要性。我们表明，权重可能不会影响阶段效率得分的计算，并且使用不同权重导致的总体效率变化可能与恒定的阶段效率有关。我们演示了隔离权重对整体效率的影响的必要性。我们提出了一种新的综合效率指标来解决加权加性效率分解中的一些缺陷。我们的研究结果用两个代表两种类型的两阶段网络结构的经验数据集来说明。(C) 2016 Elsevier B.V.版权所有
Data envelopment analysis (DEA) is a technique for performance evaluation of peer decision making units (DMUs). The network DEA models study the internal structures of DMUs. Using two-stage network structures as an example, the current paper examines additive efficiency decomposition where the overall efficiency is defined as a weighted average of stage efficiencies and the weights are used to reflect relative importance of individual stages. We show that weights may not affect the calculation of stage efficiency scores and that variation in the overall efficiency resulting from using different weights can be associated with constant stage efficiencies. We demonstrate the need to isolate the impact of weights on the overall efficiency. We propose to use a new overall efficiency index to address some pitfalls in weighted additive efficiency decomposition. Our findings are illustrated using two empirical data sets representing two types of two-stage network structures. (C) 2016 Elsevier B.V. All rights reserved.