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A general model framework for DEA

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  • Kleine, A.
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    Abstract

    Analyzing the efficiency of actions, productions, or organizational units is a fundamental problem of economic research. Data Envelopment Analysis (DEA) is designed to measure the efficiency of decision making units. The set of reference units in DEA is generated on the assumption of a specific production technology. From a decision theoretical view, DEA models are based on scalarizing functions known from Multi-Criteria Decision Making. On these grounds it is possible to formulate a general DEA model. This formulation is a basis for well-known and new DEA approaches, as a classification scheme for DEA models shows.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 32 (2004)
    Issue (Month): 1 (February)
    Pages: 17-23

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    Handle: RePEc:eee:jomega:v:32:y:2004:i:1:p:17-23

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    Related research

    Keywords: Data envelopment analysis Efficiency Activity analysis Multi-criteria decision making;

    References

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    1. Tarja Joro & Pekka Korhonen & Jyrki Wallenius, 1998. "Structural Comparison of Data Envelopment Analysis and Multiple Objective Linear Programming," Management Science, INFORMS, vol. 44(7), pages 962-970, July.
    2. 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.
    3. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
    4. 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.
    5. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
    6. Yun, Y. B. & Nakayama, H. & Tanino, T. & Arakawa, M., 2001. "Generation of efficient frontiers in multi-objective optimization problems by generalized data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 129(3), pages 586-595, March.
    7. KERSTENS, Kristiaan & VANDEN EECKAUT, Philippe, 1997. "Estimating returns to scale using nonparametric deterministic technologies : a new method based on goodness-of-fit," CORE Discussion Papers 1997013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    9. Yu, Gang & Wei, Quanling & Brockett, Patrick & Zhou, Li, 1996. "Construction of all DEA efficient surfaces of the production possibility set under the Generalized Data Envelopment Analysis Model," European Journal of Operational Research, Elsevier, vol. 95(3), pages 491-510, December.
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    Cited by:
    1. Anna Cwiakala-Malys & Wioletta Nowak, 2009. "Classification of Data Envelopment Analysis models," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 3, pages 5-18.
    2. Ehrgott, Matthias & Tind, Jørgen, 2009. "Column generation with free replicability in DEA," Omega, Elsevier, vol. 37(5), pages 943-950, October.
    3. Aparicio, Juan & Pastor, Jesus T., 2014. "Closest targets and strong monotonicity on the strongly efficient frontier in DEA," Omega, Elsevier, vol. 44(C), pages 51-57.
    4. Briec, Walter & Kerstens, Kristiaan, 2009. "Multi-horizon Markowitz portfolio performance appraisals: A general approach," Omega, Elsevier, vol. 37(1), pages 50-62, February.

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