A computational study of DEA with massive data sets
Data envelopment analysis (DEA) is computationally intensive. This work answers conclusively questions about computational performance and scale limits of the standard LP-based procedures currently used. Examples of DEA problems with up to 15K entities are documented and it is not hard to imagine problem size increasing as new more sophisticated applications are found for DEA. This work reports on a comprehensive computational study involving DEA problems with up to 100K DMUs. We explore the impact of different LP algorithms including interior point methods as well as accelerators such as advanced basis starts and DEA specific enhancements such as restricted basis entry (RBE). Our results demonstrate that solution times behave close to quadratically and that massive problems can be solved efficiently. We propose ideas for extending DEA into a data mining tool.
data envelopment analysis (dea) linear programming convex analysis