Characterization of DEA ranking models

Abstract

Other than measuring relative efficiency, DEA (Data Envelopment Analysis) has been used in a number of other ways to elaborate further on the performance of individual units or to ascertain how the units could become more efficient. Also researchers have developed methods for using DEA as a ranking model. We classified DEA ranking models into two categories based on whether preferences (weights) are given or not. When the decision maker's preferences (weights) are not given, the ranking criteria and corresponding ranking results of each model vary depending on the methods each model uses. When the decision maker's preferences (weights) are given, the accuracy and acceptability of the results depend on how well these given preferences are reflected to each weight restriction method. Since the ranking result from each model is determined by the characteristics each model has, it is important to understand these characteristics. This hopefully can help decision makers to make a better decision. In this dissertation, we analyze the characteristics of A-P (Andersen-Peterson) model and cross-efficiency evaluation in category 1, and coneratio and Wong and Beasley weight restrictions in category 2. Alternative models for measuring overall efficiency are proposed. To better characterize ranking models, we define a new metric, the specialization index (SI), and propose using the Ak score in cross-efficiency evaluation to identify specialized DMUs. Also we examine the popular characterization on the 1st ranker of cross-efficiency evaluation and show that it is not always true. The fixed weighting nature of cross-efficiency evaluation is analyzed in the multiple-input, multiple-output situation analytically and