数据包络分析方法(DEA)综述被引量：162

Review of data envelopment analysis

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摘要数据包络分析方法是一种基于投入产出数据的相对有效性评价方法.该方法中包含若干关键要素,具体包括:生产可能集,测度,偏好,变量的类型,问题的层次以及数据是否是确定的.以上这些要素的组合可以形成不同的DEA模型,用于解决不同的问题.本文旨在围绕以上关键要素对DEA方法近年的若干重要研究工作和模型进行梳理和分类,并简要介绍了DEA模型的若干应用.最后,给出未来可能的若干研究方向. The data envelopment analysis （DEA） is a multi-factor productivity analysis tool for measuring the relative efficiencies of a homogenous set of decision-making units （DMU s）, which is firstly proposed by Charnes, Cooper and Rhodes in 1978. There are several key elements in DEA models, including production possibility set, measure, preference, type of variables, level of the evaluation problem, and explicit data or not. The combination of these elements can form different DEA model, which can be used to solve different problems. This paper aims to summarize a number of important studies in recent years based on the above six key elements of the DEA models. Also, this paper briefly describes some applications of DEA models. Finally, this paper presents some Oossible future research directions.

作者杨国梁刘文斌郑海军

机构地区中国科学院科技政策与管理科学研究所英国肯特大学商学院

出处《系统工程学报》 CSCD 北大核心 2013年第6期840-860,共21页 Journal of Systems Engineering

基金国家自然科学基金资助项目(71201158) 中科院科技政策与管理科学研究所重大科研资助项目(Y201531801)

关键词数据包络分析方法相对有效性关键要素 data envelopment analysis relative efficiency key elements

分类号 C934 [经济管理—管理学]

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