Data Envelopment Analysis With Common Weights: The Compromise Solution Approach

• Main Authors: Hsi-Tai Hung, 洪僖黛
• Other Authors: Chiang Kao
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/45179731377202329980

博士 === 國立成功大學 === 工業與資訊管理學系碩博士班 === 93 === 　Data envelopment analysis (DEA) has been widely applied to measure the relative efficiency of a group of homogeneous decision making units (DMUs) with multiple inputs and multiple outputs. A characteristic of DEA is to allow individual decision making units to select the factor weights which are the most advantageous for them in calculating their efficiency scores. The DEA method essentially classifies all DMUs into two groups, viz., efficient and inefficient. As a considerable number of DMUs are usually categorized as efficient, the approach of common weights in DEA is utilized to improve the discrimination power of DEA. 　For comparing the DMUs based on a common base, this study proposes a compromise solution approach for generating common weights under the DEA framework. Moreover, some properties of the compromise solutions are explained. The efficiency scores calculated from the standard DEA model are regarded as the ideal solution for the DMUs to achieve. A common set of weights which produces the vector of efficiency scores for the DMUs closest to the ideal solution is sought. Since the DEA method can be viewed as a weighting method, the proposed compromise solution approach is modified in order to generate the weights in the multiple criteria decision making (MCDM) problem. 　To illustrate the idea of the compromise solution approach, three examples, the efficiency measurement of forest districts, the comparison of university libraries, and the construction of the composite management indices for industrial firms, are utilized. The first is an efficiency evaluation problem in the DEA context, whereas, the other two belong to the MCDM area. As a comparison, other weighting approaches are also used to generate the common weights for the examples in order to understand the differences and characteristics of different approaches.