Solving Real-World Linear Programs: A Decade and More of Progress
Operations Research
Algorithms for the Frame of a Finitely Generated Unbounded Polyhedron
INFORMS Journal on Computing
Computers and Operations Research
A procedure for large-scale DEA computations
Computers and Operations Research
A Multi-agents Approach to Knowledge Discovery
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
A unified model for detecting efficient and inefficient outliers in data envelopment analysis
Computers and Operations Research
An Algorithm for Data Envelopment Analysis
INFORMS Journal on Computing
Interior point methods in DEA to determine non-zero multiplier weights
Computers and Operations Research
Competing output-sensitive frame algorithms
Computational Geometry: Theory and Applications
An Algorithm for Data Envelopment Analysis
INFORMS Journal on Computing
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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.