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 DEA-Solver Pro   - 数据包络分析法软件
近年来已经有很多使用数据包络分析(Data Envelopment Analysis, DEA)来评估许多不同种类实体效益的多种应用,在很多不同背景的国家中从事不同的活动。一个原因是,因为经常有未知的复杂因素,很多活动牵扯到多次输入和输出之间的关系,DEA已经提供对解决这些情况的可能性。例子包括:在不同地理位置的美国空军基地的维护活动,英国和韦尔斯的警力分配,塞浦路斯和加拿大分行的效能,和美国、英国以及法国的大学实施他们的教育和研究过程中的效率。借着各式各样的输入和输出(包括把"社会"和"安全网"的支出当作输入数据,把各种方面的"生活质量"当做输出数据),这些种类的应用延伸到用来评价城市、地区和国家的实施效率。
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DEA-Solver Pro 是在 W.W. Cooper, L.M. Seiford and K. Tone 编著的书籍《Data Envelopment Analysis-A Comprehensive Text with Models, Applications, References and DEA-Solver Software》的基础上设计的。
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DEA-Solver Pro 中国代理

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支持模型
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AR模型
AR Global模型
Super-efficiency模型
NCN和NDSC模型
BND模型
CAT,SYS和Bilateral模型
Cost和New-Cost模型
Revenue和New-Revenue模型
Profit,New-Profit和Ratio模型
Window,Malmquist和Malmquist-Radial模型
Scale Elasticity模型
Congestion模型
Undesirable Output模型
Weight SBM模型
Hybrid模型
Network DEA(Network SBM)模型
Dynamic DEA(Dynamic SBM)模型
Dynamic和Network SBM(DNSBM)模型
Non-Convex模型
Resampling DEA模型
Directional Distance模型
SBM Max模型
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Latest Releases
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The new release of version 13 is 13c, with a new feature SBM Max model, which replaces SBM Variation model introduced in version 12. The SBM models usually report the worst efficiency scores for inefficient DMUS, i.e. the projected point is the farthest one on the associated ewfficient frontier. In contrast, SBM max models look for the nearest point on the efficient frontier, so that one can attain an efficient status by minimum input resources and output expansion. Consequently, SBM Max represents KAIZEN improvement. For more details, go to newsletter 15.
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The final release for version 12 is 12h, which repairs minor problems of the CCR model. In the release 12e, we introduced a new feature, called SBM Variations, by which you can find the nearest point on the associated efficient frontier by maximizing the efficiency score. For more details, go to newsletter 14. Note that in Version 12, we also added a feature, Directional Distance Model, which computes the efficiency of DMUs along the given direction of DMU. We offer two types of efficiency calculations, i.e. the ordinary one and the super efficiency. This is an innovative extension of Radial Models (CCR and BCC), which had limited orientations of input and output. For detailed discussion on this model, take a look at Newsletter 13.
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With Version 11, we added a feature, called Resampling DEA, which deals with data variations and gives confidence intervals of DEA scores. Also we introduced Fisher statistics in version 11.1, which enhance resampling accuracy (Note the number of DMU has to be at least 4 to use Fisher statistics). Input/output values are subject to change for such reasons as measurement errors and arbitrariness. DEA efficiency scores need to be examined by considering such factors, and resampling calculates the confidence interval by four new innovative models. Take a look at Newsletter 12 for more details.
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Version 10, we added a feature, called Non-Convex model, which deals with S-shaped curve frontiers in production. Such S-shaped frontiers, often observed in many real data sets, are non-convex whereas traditional DEA models assume convex frontiers. This model should solve the large difference between the constant-returns-to-scale and variable-returns-to-scale scores that have puzzled many practitioners. Take a look at Newsletter 11 for more details.
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For downloading all the newsletters, click DEA SolverPro™ Newsletters.
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