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Fuzzy Pair-wise Dominance and Implications for Technical Efficiency Performance Assessment

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  • Konstantinos Triantis
  • Philippe Eeckaut

Abstract

Classically, the concept of efficiency measurement is based on the definition of a frontier that envelops the observed production plans. The efficiency score itself is based on the distance of an observed production plan from this frontier. The frontier along with the required technological assumptions (such as convexity) needed for its definition may be replaced with the concept of pair-wise dominance. This concept leads to a classification scheme for all production plans instead of a ranking based on efficiency scores. Also, the traditional assumption of deterministic or crisp production plans may be substituted with the weaker assumption of fuzzy production plans as proposed by fuzzy set theory. This paper merges these two concepts and defines a new classification scheme based on fuzzy dominance. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Konstantinos Triantis & Philippe Eeckaut, 2000. "Fuzzy Pair-wise Dominance and Implications for Technical Efficiency Performance Assessment," Journal of Productivity Analysis, Springer, vol. 13(3), pages 207-230, May.
    • Handle: RePEc:kap:jproda:v:13:y:2000:i:3:p:207-230
    DOI: 10.1023/A:1007870908622
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    References listed on IDEAS

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    1. Triantis, Konstantinos & Sarangi, Sudipta & Kuchta, Dorota, 2003. "Fuzzy pair-wise dominance and fuzzy indices: An evaluation of productive performance," European Journal of Operational Research, Elsevier, vol. 144(2), pages 412-428, January.
    2. Henry Tulkens & Philippe Eeckaut, 2006. "Non-Frontier Measures of Efficiency, Progress and Regress for Time Series Data," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 373-394, Springer.
    3. Konstantinos Triantis & Olivier Girod, 1998. "A Mathematical Programming Approach for Measuring Technical Efficiency in a Fuzzy Environment," Journal of Productivity Analysis, Springer, vol. 10(1), pages 85-102, July.
    4. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 1993. "The Measurement of Productive Efficiency: Techniques and Applications," OUP Catalogue, Oxford University Press, number 9780195072181.
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    1. Triantis, Konstantinos & Sarangi, Sudipta & Kuchta, Dorota, 2003. "Fuzzy pair-wise dominance and fuzzy indices: An evaluation of productive performance," European Journal of Operational Research, Elsevier, vol. 144(2), pages 412-428, January.
    2. Adel Hatami-Marbini & Per J. Agrell & Hirofumi Fukuyama & Kobra Gholami & Pegah Khoshnevis, 2017. "The role of multiplier bounds in fuzzy data envelopment analysis," Annals of Operations Research, Springer, vol. 250(1), pages 249-276, March.
    3. Bill Seaver & Konstantinos Triantis & Barbara Hoopes, 2004. "Efficiency Performance and Dominance in Influential Subsets: An Evaluation using Fuzzy Clustering and Pair-wise Dominance," Journal of Productivity Analysis, Springer, vol. 21(2), pages 201-220, March.
    4. de Sousa, Maria da Conceição Sampaio & Cribari-Neto, Francisco & Stosic, Borko D., 2005. "Explaining DEA Technical Efficiency Scores in an Outlier Corrected Environment: The Case of Public Services in Brazilian Municipalities," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 25(2), November.

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