Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis (repost)

In a relatively short period of time, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. It has been successfully applied to a whole variety of problems in many different contexts worldwide. The analysis of an array of these problems has been resistant to other methodological approaches because of the multiple levels of complexity that must be considered. Several examples of multifaceted problems in which DEA analysis has been successfully used are: (1) maintenance activities of US Air Force bases in geographically dispersed locations, (2) policy force efficiencies in the United Kingdom, (3) branch bank performances in Canada, Cyprus, and other countries and (4) the efficiency of universities in performing their education and research functions in the U.S., England, and France. In addition to localized problems, DEA applications have been extended to performance evaluations of 'larger entities' such as cities, regions, and countries. These extensions have a wider scope than traditional analyses because they include "social" and "quality-of-life" dimensions which require the modeling of qualitative and quantitative data in order to analyze the layers of complexity for an evaluation of performance and to provide solution strategies.