.png){kind=icon}
返回
A procedure for large-scale DEA computations
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cor.2008.05.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data envelopment analysis (DEA), a performance evaluation method, measures the relative efficiency of a particular decision making unit (DMU) against a peer group. Most popular DEA models can be solved using standard linear programming (LP) techniques and therefore, in theory, are considered as computationally easy. However, in practice, the computational load cannot be neglected for large-scale-in terms of number of DMUs-problems. This study proposes an accelerating procedure that properly identifies a few similar critical DMUs to compute DMU efficiency scores in a given set. Simulation results demonstrate that the proposed procedure is suitable for solving large-scale BCC problems when the percentage of efficient DMUs is high. The computational benefits of this procedure are significant especially when the number of inputs and outputs is small, which are most widely reported in the literature and practices. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Data envelopment analysis
Computational efficiency
Large-scale LP problems
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.3
论文数:
6.5K
被引数:
1.8W
机构
引用论文
Decomposition of the Fisher Ideal index of productivity: A non-parametric dual analysis of US airline data.png)
ECONOMIC JOURNAL
ECONOMIC JOURNALIF3.6
PREAI
分享收藏A Novel Approach to Calculating Energy Density from Food Images Reduces Analysis Time and Cost一种从食物图像计算能量密度的创新方法，减少了分析时间和成本
OAAI
OAAI分享收藏On the resistivity and surface tension of the eutectic alloy of gallium and indium关于镓铟共晶合金的电阻率和表面张力
PREAI
分享收藏
查看更多内容