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数据包络分析模型40年发展综述：历史沿革与当前趋势

A Review on the 40 Years of Existence of Data Envelopment Analysis Models: Historic Development and Current Trends.

作者信息

Panwar Ankita, Olfati Maryam, Pant Millie, Snasel Vaclav

机构信息

Department of Applied Mathematics and Scientific Computing, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand India.

Electrical Engineering and Computer Science, Department of Computer Science, Technical University of Ostrava, Ostrava, Czech Republic.

出版信息

Arch Comput Methods Eng. 2022;29(7):5397-5426. doi: 10.1007/s11831-022-09770-3. Epub 2022 Jun 10.

DOI:10.1007/s11831-022-09770-3
PMID:35702633
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9184254/
Abstract

DEA, incepted in 80s, has emerged as a popular decision-making technique, for determining the efficiency of similar units. Due to its simplicity and applicability, DEA has gained the attention of scientists and researchers working in diverse areas, which has contributed towards a rich literature both in terms of theoretical development as well as different applications. This paper tries to bring together the near 40 years of existence of DEA in a concise format by discussing the popular DEA models, their advantages and shortcomings, and different applications of DEA. It also provides a brief bibliometric analysis to highlight the development of DEA over the years in terms of publication trends, highly cited papers, journal citation, etc.

摘要

数据包络分析（DEA）始于20世纪80年代，已成为一种流行的决策技术，用于确定相似单元的效率。由于其简单性和适用性，DEA受到了不同领域科学家和研究人员的关注，这在理论发展和不同应用方面都促成了丰富的文献。本文试图通过讨论流行的DEA模型、它们的优缺点以及DEA的不同应用，以简洁的形式总结DEA近40年的发展历程。它还提供了简要的文献计量分析，以从发表趋势、高被引论文、期刊引用等方面突出DEA多年来的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/deb5c5629cde/11831_2022_9770_Fig12_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/5ac48e7c779c/11831_2022_9770_Fig9_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/e87c84f9d0c1/11831_2022_9770_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/deb5c5629cde/11831_2022_9770_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/aa7307c87919/11831_2022_9770_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/b34f0329b405/11831_2022_9770_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/af6e58c6f7b4/11831_2022_9770_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/48e40eead5b8/11831_2022_9770_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/f82a10cc9243/11831_2022_9770_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/2a0bc19d7f39/11831_2022_9770_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/380c3db9fa0d/11831_2022_9770_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/6f881a63676b/11831_2022_9770_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/5ac48e7c779c/11831_2022_9770_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/b1d536bddcf4/11831_2022_9770_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/e87c84f9d0c1/11831_2022_9770_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/9184254/deb5c5629cde/11831_2022_9770_Fig12_HTML.jpg

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