Large-Scale Data Envelopment Analysis Models and Related Applications

摘要

This dissertation presents several advances in data envelopment analysis (DEA), amethod for assessing the efficiency of decision units through the identification of empirical best-practice frontiers. First, a new hierachical decomposition approach for solving large-scale problems is described with results of computational testing in both serial and parallel environments, that dramatically reduces the solution time for realistic DEA applications. Second, a new set of models for stratifying and ranking decision units provides important newer insights into relationships among the units than what was possible with traditional frontier analysis. Because of the intensive computational requirements of these models, their practicality builds on the effectiveness of hierarchical process. Finally, a new means of assessing the robustness of a decision-unit's efficiency is given which spans all current models and assists managers in their evaluation of process and organizational improvement options. It is expected that these advances will permit practitioners and researchers to be more expansive and ambitious in their use of this important class of models, and will hopefully encourage new and even more exciting applications of DEA.