基于公共权重数据包络分析的军事训练绩效排序

doi:10.11772/j.issn.1001-9081.2015.04.1196

计算机应用 ›› 2015, Vol. 35 ›› Issue (4): 1196-1199.DOI: 10.11772/j.issn.1001-9081.2015.04.1196

基于公共权重数据包络分析的军事训练绩效排序

张有亮¹, 张宏军¹, 张睿¹, 杨波江², 曾子林^1,3, 郭利生⁴

1. 解放军理工大学指挥信息系统学院, 南京 210007;
2. 解放军理工大学, 南京 210007;
3. 南昌陆军学院, 南昌 330103;
4. 中国人民解放军66242部队, 呼和浩特 011216

收稿日期:2014-10-22 修回日期:2014-12-11 出版日期:2015-04-10 发布日期:2015-04-08
通讯作者: 张有亮
作者简介:张有亮(1986-),男,河北邯郸人,博士研究生,主要研究方向: 军事训练绩效评估、数据包络分析; 张宏军(1963-),男,江苏泰州人,教授,博士生导师,博士,主要研究方向: 军事训练数据、知识工程; 张睿(1977-),男,山东文登人,副教授,博士,主要研究方向:军事训练数据采集、军事建模; 杨波江(1974-),男,江苏连云港人,硕士,主要研究方向:军事训练数据处理; 曾子林(1981-),女,江西鄱阳人,讲师,博士研究生,主要研究方向:效能评估、机器学习; 郭利生(1978-),男,内蒙包头人,高级工程师,硕士,主要研究方向:军事训练、军事仿真。
基金资助:
国家自然科学基金资助项目(70971137); 国家社会科学基金资助项目(13QJ004-098)。

Ranking of military training performances based on data envelopment analysis of common weights

ZHANG Youliang¹, ZHANG Hongjun¹, ZHANG Rui¹, YANG Bojiang², ZENG Zilin^1,3, GUO Lisheng⁴

1. Institute of Command and Information Systems, PLA University of Science and Technology, Nanjing Jiangsu 210007, China;
2. PLA University of Science and Technology, Nanjing Jiangsu 210007, China;
3. Nanchang Military Academy, Nanchang Jiangxi 330103, China;
4. Unit 66242 of PLA, Hohhot Nei Mongol 011216, China

Received:2014-10-22 Revised:2014-12-11 Online:2015-04-10 Published:2015-04-08

摘要/Abstract

摘要：

针对传统数据包络分析(DEA)公共权重生成方法不同时具备线性、规模无关优点的问题,根据军事训练绩效评估需求,提出了一种新的DEA公共权重生成方法。该方法以DEA有效单位为计算基础,首先对训练数据进行归一化,然后运用多目标规划模型求解,绩效排序结果更加公平合理,并且同时具有线性、规模无关的优点。最后,通过一个军事应用,证明了该方法科学、有效。

关键词: 军事训练, 绩效, 排序, 数据包络分析, 公共权重, 线性, 多目标规划

Abstract:

Conventional approaches for Common Weights (CW) generation in Data Envelopment Analysis (DEA) are either non-linear or scale-relevant. To solve this problem, according to the demand of military training performance evaluation, a new method was proposed to generate CW in DEA. The new method took DEA efficient units as the basis of calculation. Firstly, training data were normalized, and then multi-objective programing was employed for CW generation, which can lead to a fairer and more reasonable ranking of performances. The proposed method is not only linear, but also scale-irrelevant. Lastly, a military application illustrates that the proposed method is scientific and effective.

Key words: military training, performance, ranking, Data Envelopment Analysis (DEA), common weight, linear, multi-objective programing

中图分类号:

N945.16
E917

张有亮, 张宏军, 张睿, 杨波江, 曾子林, 郭利生. 基于公共权重数据包络分析的军事训练绩效排序[J]. 计算机应用, 2015, 35(4): 1196-1199.

ZHANG Youliang, ZHANG Hongjun, ZHANG Rui, YANG Bojiang, ZENG Zilin, GUO Lisheng. Ranking of military training performances based on data envelopment analysis of common weights[J]. Journal of Computer Applications, 2015, 35(4): 1196-1199.

参考文献

[1] CHARNES A, COOPER W W, RHODES E. Measuring the efficiency of decision making units[J]. European Journal of Operational Research, 1978, 2(6): 429-444.
[2] LIU J, LU L Y, LU W, et al. A survey of DEA applications[J]. Omega, 2013, 41(5): 893-902.
[3] COOK W D, ROLL Y, KAZAKOV A. A DEA model for measuring the relative efficiency of highway maintenance patrols[J]. Information Systems and Operational Research, 1990, 28(2): 113-124.
[4] KARSAK E E, AHISKA S S. Improved common weight MCDM model for technology selection[J]. International Journal of Production Research, 2008, 46(24): 6933-6944.
[5] FOROUGHI A A. A modified common weight model for maximum discrimination in technology selection[J]. International Journal of Production Research, 2012, 50(14): 3841-3846.
[6] YANG F, YANG C, LIANG L, et al. New approach to determine common weights in DEA efficiency evaluation model[J]. Journal of Systems Engineering and Electronics, 2010, 21(4): 609-615.
[7] WANG Y, LUO Y, LAN Y. Common weights for fully ranking decision making units by regression analysis[J]. Expert Systems with Applications, 2011, 38(8): 9122-9128.
[8] YANG F, YANG C, LIANG L, et al. Ranking decision making units using common-weight DEA model[J]. Journal of Systems Engineering, 2011, 26(4): 551-557.(杨锋, 杨琛琛, 梁樑, 等. 基于公共权重DEA模型的决策单元排序研究[J]. 系统工程学报, 2011, 26(4): 551-557.)
[9] LIU F H F, PENG H H. Ranking of units on the DEA frontier with common weights [J]. Computers and Operations Research, 2008, 35(5): 1624-1637.
[10] CHEN Y, LARBANI M, CHANG Y. Multiobjective data envelopment analysis [J]. Journal of the Operational Research Society, 2009, 60(11): 1556-1566.
[11] JAHANSHAHLOO G R, LOTFI F H, KHANMOHAMMADI M, et al. Ranking of units by positive ideal DMU with common weights[J]. Expert Systems with Applications, 2010, 37(12): 7483-7488.
[12] CHIANG C I, HWANG M J, LIU Y H. Determining a common set of weights in a DEA problem using a separation vector[J]. Mathematical and Computer Modelling, 2011, 54(9/10): 2464-2470.
[13] SUN J, WU J, GUO D. Performance ranking of units considering ideal and anti-ideal DMU with common weights[J]. Applied Mathematical Modelling, 2013, 37(9): 6301-6310.
[14] ROLL Y, GOLANY B. Alternate methods of treating factor weights in DEA[J]. Omega, 1993, 21(1): 99-109.

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基于公共权重数据包络分析的军事训练绩效排序

Ranking of military training performances based on data envelopment analysis of common weights

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