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Determining radial efficiency with a large data set by solving small-size linear programs

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  • Wen-Chih Chen

    (National Chiao Tung University)

  • Sheng-Yung Lai

    (National Chiao Tung University)

Abstract

This paper presents a new algorithm for determining radial efficiency with a large data set by using small-size linear programs (LPs). Instead of trying to “reduce” the size of individual LPs, the proposed algorithm attempts to “control” the size of individual LPs, e.g., no more than 100 data points each time while maintaining the solution quality. The algorithm is specifically designed to address the problem of LP size limitation. From the empirical results, we conclude that the proposed algorithm can converge within a reasonable number of iterations without incurring extra computation time and has savings of up to 60 % of the benchmarks when the data set contains 15,000 points.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:250:y:2017:i:1:d:10.1007_s10479-015-1968-4
    DOI: 10.1007/s10479-015-1968-4
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. 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.
    3. Chen, Yao & Iqbal Ali, Agha, 2002. "Output-input ratio analysis and DEA frontier," European Journal of Operational Research, Elsevier, vol. 142(3), pages 476-479, November.
    4. 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.
    5. O. B. Olesen & N. C. Petersen, 1996. "Indicators of Ill-Conditioned Data Sets and Model Misspecification in Data Envelopment Analysis: An Extended Facet Approach," Management Science, INFORMS, vol. 42(2), pages 205-219, February.
    6. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    7. Ali, Agha Iqbal, 1993. "Streamlined computation for data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 64(1), pages 61-67, January.
    8. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, October.
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    Cited by:

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