Ranking Models and Discrimination Improvement in DEA
碩士 === 國立臺北科技大學 === 商業自動化與管理研究所 === 94 === Data Envelopment Analysis (DEA) is a tool of performance assessment manipulating multiple inputs and outputs without giving subjective weights of each input and output in advance. Although DEA has been applied in many fields to evaluate performance, the met...
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| Format: | Others |
| Language: | zh-TW |
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2006
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| Online Access: | http://ndltd.ncl.edu.tw/handle/x89z32 |
| Summary: | 碩士 === 國立臺北科技大學 === 商業自動化與管理研究所 === 94 === Data Envelopment Analysis (DEA) is a tool of performance assessment manipulating multiple inputs and outputs without giving subjective weights of each input and output in advance. Although DEA has been applied in many fields to evaluate performance, the method still has following problems. i.e., (1) Result in to many efficient decision making units (DMUs) , i.e., low discrimination. (2) Obtain many sets of weight for DMUs. (3) Consider absolute efficiency instead of relative efficiency.
This study adopts weight normalization approach to treat the problem (1) to increase the discrimination of DEA. To deal with problem (2) this study present a common weight model to find a set of weight for all DMUs with the maximum score. A rank minimization model is developed to assess the DMUs to eliminate the problem (3) mentioned above.
Practical examples are also presented to illustrate the differences between the DEA and the proposed method.
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