Data envelopment analysis: Prior to choosing a model
Data envelopment analysis: Prior to choosing a model
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DOI：
10.1016/j.omega.2013.09.004
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发表时间：
2014-04-01
影响因子：
6.9
通讯作者：
Zhu, Joe
中科院分区：
文献类型：
--
作者：
Cook, Wade D.;Tone, Kaoru;Zhu, Joe
在本文中，我们将讨论与数据包络分析（DEA）的使用有关的几个问题。这些问题包括模型定位、输入和输出的选择/定义、混合数据和原始数据的使用，以及要使用的输入和输出的数量与决策单元的数量之间的关系。我们认为，在DEA社区内，研究人员，从业人员和评审人员可能会对这些问题表示担忧，并且在许多情况下，对这些问题有不正确的看法。有些关切来自被认为是DEA工作的目的。虽然DEA前沿可以被正确地视为生产前沿，但必须记住，DEA最终是一种绩效评估和对最佳实践进行基准测试的方法。DEA可以被看作是一种工具，用于多标准评价问题，其中DMU是备选方案，每个DMU由其在多个标准中的表现表示，这些标准被称为/分类为DEA输入和输出。本文件的目的是就这些问题提供一些澄清和指导。(C)2013爱思唯尔有限公司保留所有权利。
In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have concerns and, in many cases, incorrect views about these issues. Some of the concerns stem from what is perceived as being the purpose of the DEA exercise. While the DEA frontier can rightly be viewed as a production frontier, it must be remembered that ultimately DEA is a method for performance evaluation and benchmarking against best-practice. DEA can be viewed as a tool for multiple-criteria evaluation problems where DMUs are alternatives and each DMU is represented by its performance in multiple criteria which are coined/classified as DEA inputs and outputs. The purpose of this paper is to offer some clarification and direction on these matters. (C) 2013 Elsevier Ltd. All rights reserved.