Data envelopment analysis with common weights: The compromise solution approach

C. Kao, H. T. Hung

Research output: Contribution to journalArticlepeer-review

216 Citations (Scopus)


A characteristic of data envelopment analysis (DEA) is to allow individual decision-making units (DMUs) to select the factor weights that are the most advantageous for them in calculating their efficiency scores. This flexibility in selecting the weights, on the other hand, deters the comparison among DMUs on a common base. In order to rank all the DMUs on the same scale, this paper proposes a compromise solution approach for generating common weights under the DEA framework. 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. Based on the generalized measure of distance, a family of efficiency scores called 'compromise solutions' can be derived. The compromise solutions have the properties of unique solution and Pareto optimality not enjoyed by the solutions derived from the existing methods of common weights. An example of forest management illustrates that the compromise solution approach is able to generate a common set of weights, which not only differentiates efficient DMUs but also detects abnormal efficiency scores on a common base.

Original languageEnglish
Pages (from-to)1196-1203
Number of pages8
JournalJournal of the Operational Research Society
Issue number10
Publication statusPublished - 2005 Oct 1

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Strategy and Management
  • Management Science and Operations Research
  • Marketing


Dive into the research topics of 'Data envelopment analysis with common weights: The compromise solution approach'. Together they form a unique fingerprint.

Cite this