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Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA)

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  • K S Park

    (University of Ulsan)

Abstract

This note is concerned with joint uses of imprecise data and assurance regions (ARs) in data envelopment analysis (DEA), referred to as assurance region-imprecise DEA (AR-IDEA). It has been developed to transform the AR-IDEA models into ordinary linear programming equivalents via scale transformations and variable alterations plus introducing dummy variables. In this note, we show one simpler approach for achieving linear programming equivalents only by variable alterations without rescaling as well as introducing dummy variables. We also provide findings in the use of imprecise data and AR conditions in DEA. This points out that some AR conditions do not affect the efficiency ratings under AR-IDEA.

Suggested Citation

  • K S Park, 2004. "Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA)," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1363-1366, December.
    • Handle: RePEc:pal:jorsoc:v:55:y:2004:i:12:d:10.1057_palgrave.jors.2601824
    DOI: 10.1057/palgrave.jors.2601824
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Huang, Z. M. & Sun, D. B., 1990. "Polyhedral Cone-Ratio DEA Models with an illustrative application to large commercial banks," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 73-91.
    2. Thompson, Russell G. & Dharmapala, P. S. & Thrall, Robert M., 1995. "Linked-cone DEA profit ratios and technical efficiency with application to Illinois coal mines," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 99-115, April.
    3. William W. Cooper & Kyung Sam Park & Gang Yu, 1999. "IDEA and AR-IDEA: Models for Dealing with Imprecise Data in DEA," Management Science, INFORMS, vol. 45(4), pages 597-607, April.
    4. William W. Cooper & Kyung Sam Park & Gang Yu, 2001. "An Illustrative Application of Idea (Imprecise Data Envelopment Analysis) to a Korean Mobile Telecommunication Company," Operations Research, INFORMS, vol. 49(6), pages 807-820, December.
    5. Thompson, Russell G. & Langemeier, Larry N. & Lee, Chih-Tah & Lee, Euntaik & Thrall, Robert M., 1990. "The role of multiplier bounds in efficiency analysis with application to Kansas farming," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 93-108.
    6. W W Cooper & K S Park & G Yu, 2001. "IDEA (Imprecise Data Envelopment Analysis) with CMDs (Column Maximum Decision Making Units)," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(2), pages 176-181, February.
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    Cited by:

    1. K S Park, 2007. "Efficiency bounds and efficiency classifications in DEA with imprecise data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(4), pages 533-540, April.
    2. Jens Leth Hougaard & Pieter Jan Kerstens & Kurt Nielsen, 2019. "Benchmarking with uncertain data: a simulation study comparing alternative methods," IFRO Working Paper 2019/05, University of Copenhagen, Department of Food and Resource Economics.
    3. HATAMI-MARBINI, Adel & AGRELL, Per & AGHAYI, Nazila, 2013. "Imprecise data envelopment analysis for the two-stage process," LIDAM Discussion Papers CORE 2013004, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Park, K. Sam, 2010. "Duality, efficiency computations and interpretations in imprecise DEA," European Journal of Operational Research, Elsevier, vol. 200(1), pages 289-296, January.
    5. Reza Farzipoor Saen, 2009. "A decision model for ranking suppliers in the presence of cardinal and ordinal data, weight restrictions, and nondiscretionary factors," Annals of Operations Research, Springer, vol. 172(1), pages 177-192, November.

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