A Computational Framework for Accelerating DEA

J.H. Dulá and R.M. Thrall

Journal of Productivity Analysis, 2001, vol. 16, issue 1, 63-78

Abstract: We introducea new computational framework for DEA that reduces computationtimes and increases flexibility in applications over multiplemodels and orientations.The process is based on the identificationof frames--minimal subsets of the data needed to describethe models in the problems--for each of the four standardproduction possibility sets. It exploits the fact that the framesof the models are closely interrelated. Access to a frame ofa production possibility set permits a complete analysis in asecond phase for the corresponding model either oriented or orientation-free.This second phase proceeds quickly especially if the frame isa small subset of the data points. Besides accelerating computations,the new framework imparts greater flexibility to the analysisby not committing the analyst to a model or orientation whenperforming the bulk of the calculations. Computational testingvalidates the results and reveals that, with a minimum additionaltime over what is required for a full DEA study for a given modeland specified orientation, one can obtain the analysis for thefour models and all orientations. Copyright Kluwer Academic Publishers 2001

Keywords: DEA; DEA computations; linear programming; and convex analysis (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (13)

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DOI: 10.1023/A:1011103303616

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