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Parallel and hierarchical decomposition approaches for solving large-scale Data Envelopment Analysis models

Author

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  • Richard Barr
  • Matthew Durchholz

Abstract

Accompanying the increasing popularity of DEA are computationally challenging applications: large-scale problems involving the solution of thousands of linear programs. This paper describes a new problem decomposition procedure which dramatically expedites the solution of these computationally intense problems and fully exploits parallel processing environments. Testing of a new DEA code based on this approach is reported for a wide range of problems, including the largest reported to date: an 8,700-LP banking-industry application. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • Richard Barr & Matthew Durchholz, 1997. "Parallel and hierarchical decomposition approaches for solving large-scale Data Envelopment Analysis models," Annals of Operations Research, Springer, vol. 73(0), pages 339-372, October.
    • Handle: RePEc:spr:annopr:v:73:y:1997:i:0:p:339-372:10.1023/a:1018941531019
    DOI: 10.1023/A:1018941531019
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    Citations

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    Cited by:

    1. Qiwei Xie & Yuanyuan Li & Lizheng Wang & Chao Liu, 2018. "Improving discrimination in data envelopment analysis without losing information based on Renyi’s entropy," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 1053-1068, December.
    2. 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.
    3. López, Francisco J., 2011. "Generalizing cross redundancy in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 214(3), pages 716-721, November.
    4. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    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. 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.
    7. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    8. Khezrimotlagh, Dariush & Cook, Wade D. & Zhu, Joe, 2020. "A nonparametric framework to detect outliers in estimating production frontiers," European Journal of Operational Research, Elsevier, vol. 286(1), pages 375-388.
    9. Pendharkar, Parag C., 2015. "Cost minimizing target setting heuristics for making inefficient decision-making units efficient," International Journal of Production Economics, Elsevier, vol. 162(C), pages 1-12.
    10. 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.
    11. 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.
    12. Dulá, J.H. & López, F.J., 2013. "DEA with streaming data," Omega, Elsevier, vol. 41(1), pages 41-47.
    13. 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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