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A Computational Framework for Accelerating DEA

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  • J.H. Dulá
  • R.M. Thrall

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

Suggested Citation

  • J.H. Dulá & R.M. Thrall, 2001. "A Computational Framework for Accelerating DEA," Journal of Productivity Analysis, Springer, vol. 16(1), pages 63-78, July.
    • Handle: RePEc:kap:jproda:v:16:y:2001:i:1:p:63-78
    DOI: 10.1023/A:1011103303616
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Emrouznejad, Ali & De Witte, Kristof, 2010. "COOPER-framework: A unified process for non-parametric projects," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1573-1586, December.
    2. Bougnol, M.-L. & Dulá, J.H., 2009. "Anchor points in DEA," European Journal of Operational Research, Elsevier, vol. 192(2), pages 668-676, January.
    3. Tao Jie, 2020. "Parallel processing of the Build Hull algorithm to address the large-scale DEA problem," Annals of Operations Research, Springer, vol. 295(1), pages 453-481, December.
    4. José H. Dulá & Francisco J. López, 2006. "Algorithms for the Frame of a Finitely Generated Unbounded Polyhedron," INFORMS Journal on Computing, INFORMS, vol. 18(1), pages 97-110, February.
    5. Alexander P. Afanasiev & Vladimir E. Krivonozhko & Andrey V. Lychev & Oleg V. Sukhoroslov, 2020. "Multidimensional frontier visualization based on optimization methods using parallel computations," Journal of Global Optimization, Springer, vol. 76(3), pages 563-574, March.
    6. Meng Zhang & Jin-chuan Cui, 2016. "The extension and integration of the inverse DEA method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1212-1220, September.
    7. Khezrimotlagh, Dariush & Zhu, Joe & Cook, Wade D. & Toloo, Mehdi, 2019. "Data envelopment analysis and big data," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1047-1054.
    8. F J López & J H Dulá, 2008. "Adding and removing an attribute in a DEA model: theory and processing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(12), pages 1674-1684, December.
    9. Wen-Chih Chen & Sheng-Yung Lai, 2017. "Determining radial efficiency with a large data set by solving small-size linear programs," Annals of Operations Research, Springer, vol. 250(1), pages 147-166, March.
    10. Dulá, J.H. & López, F.J., 2013. "DEA with streaming data," Omega, Elsevier, vol. 41(1), pages 41-47.
    11. J. H. Dulá, 2011. "An Algorithm for Data Envelopment Analysis," INFORMS Journal on Computing, INFORMS, vol. 23(2), pages 284-296, May.

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