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Preference, Production and Performance in Data Envelopment Analysis

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Abstract

This paper attempts to provide a systematic approach to the DEA model building. To this end, we try to identify some essential aspects of DEA modelling. Three key building blocks in a DEA model are identified: they are preference order, production possibility set and performance measure. It is shown that the preferences and performance measurements used in the standard DEA models are only particular examples in this framework. It is also illustrated in this work that this methodology is useful in building new DEA models to handle nonstandard applications such as those involve non-Pareto preferences or undesirable inputs-outputs.

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References

  • Ali, A. and L.M. Seiford. (1990). “Translation Invariance in Data Envelopment Analysis.” Operations Research Letters, 10, 403–405.

    Google Scholar 

  • Allen, K. (1998). “DEA in the Ecological Context—An Overview.” In: Working Paper in European Symposium on Data Envelopment Analysis, Fachhochschule Harz, Wernigerode.

  • Allen, R.A. Athanassopoulos, R.G. Dyson, and E. Thanassoulis. (1997). “Weights Restrictions and Judgements in Data Envelopment Analysis: Evolution, Development and Future Directions.” Annals of Operations Research, 66, 93–102.

  • Banker, R.D., A. Charnes, and W.W. Cooper. (1984). “Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis.” Management Science, 30, 1078–1092.

    Google Scholar 

  • Charnes, A., W.W. Cooper, B. Golany, L. Seiford, and J. Stutz. (1985). “Foundations of Data Envelopment Analysis for Pareto-Koopmans Efficient Empirical Production Functions.” Journal of Econometrics, 30, 91–107.

    Article  Google Scholar 

  • Charnes, A., W.W. Cooper, A.Y., Lewin, and L.M. Seiford. (1994). Data Envelopment Analysis. Kluwer Academic Publishers, Dordrecht.

  • Charnes, A., W.W. Cooper, and E. Rhodes. (1978). “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research, 2, 429–444.

    Article  Google Scholar 

  • Cooper W.W., L.M. Seiford, and K. Tone. (2000). DEA Envelopment Analysis. Kluwer Academic Publishers, Boston.

    Google Scholar 

  • Charnes, A., W.W. Cooper, Q.L. Wei, and Z.M. Huhng. (1989). “Cone Ratio Data Envelopment Analysis and Multi-Objective Programming.” Int. J. Systems. Sci., 20, 1099–1118.

    Article  Google Scholar 

  • Fare, R. and C.A. Lovell. (1978). “Measuring the Technical Efficiency of Production.” Journal of Economic Theory, 19, 150–162.

    Article  Google Scholar 

  • Ferrier, G.D., K. Kerstens, and P.V. Vorden Eeckaut. (1994). “Radial and Nonradial Technical Efficiency Measures on a Data Reference Technology.” Recherches Economiques de Louvain, 60, 449–479.

    Google Scholar 

  • Golany, B. (1988). “An Interactive MOLP Procedure for the Extension of DEA to Effectiveness Analysis.” Journal of the Operational Research Society, 39, 725–734.

    Google Scholar 

  • Halme, M., T. Joro, and P. Korhonen. (1999). “Value Efficiency Analysis for Incorporating Preference Information in Data Envelopment Analysis.” Management Science, 45, 103–115.

    Article  Google Scholar 

  • Lins, M.P.E, et al. (2003). “Olympic Ranking Based on a Zero Sum Gains DEA Model.” European Journal of Operational Research, 148, 312–322.

    Article  Google Scholar 

  • Liu, P.L. (1985). Multiple Decision Making. Plenum, New York.

    Google Scholar 

  • Liu, W.B. and W. Meng. (2005). “Using DEA to Evaluate Scale Efficiency of Research Groups.” to be published in Scientific Research Management, Vol.4.

  • Liu, W.B. and J. Sharp. (1999). “DEA Models Via Goal Programming.” In G. Westerman, (ed.), Data Envelopment Analysis in the Public and Private Sector, Deutscher Universtats-Verlag.

  • Mas-colell, A., M.D. Whinston, and J.R. Green. (1995). Microeconomic Theory. Oxford University Press.

  • Nemhauser, G.L., A.R. Kan, and M.J. Todd. (1989). Handbooks in O.R. and Management Sciences. Vol. 1, Chapter 10, North Holland.

  • Pastor, J.T. (1996). “Translation Invariance in Data Envelopment Analysis.” Annals of Operations Research, 66, 93–102.

    Article  Google Scholar 

  • Pastor, J.T. and R.I. Sirvent. (1999). “An Enhanced DEA Russell Graph Efficiency Measure.” European Journal of Operational Research, 115, 596–607.

    Article  Google Scholar 

  • Podinovski, V.V. (2004). “Suitability and Redundancy of Non-Homogeneous Weight Restrictions for Measuring the Relative Efficiency in DEA.” European Journal of Operational Research, 154, 380–395.

    Article  Google Scholar 

  • Russell, R. (1988). “On the Axiomatic Approach to the Measurement of Technical Efficiency.” In: W. Eichhorn (ed.), Measurement in Economics. Heidelberg, Physica-Verlag, pp. 207–217.

  • Scheel, H. (1998). “Negative Data and Undesirable Outputs in DEA.” Working Paper in EURO Summer Institute.

  • Seiford, L.M. (1996). “Date Envelopment Analysis: Evolution of the State-of-the-art (1978–1998).” Journal of Productivity Analysis, 7, 99–137.

    Article  Google Scholar 

  • Thanassoulis, E. (2001). Introduction to the Theory and Applications of Data Envelopment Analysis. Kluwer Academic Publishers, Boston.

    Google Scholar 

  • Thanassoulis, E. and R.G. Dyson. (1992). “Estimating Preferred Target Input-Output Level Using Data Envelopment Analysis.” European Journal of Operational Research, 56, 80–97.

    Article  Google Scholar 

  • Wei, Q.L. and G. Yu. (1997). “Analysis the Properties of K-cone in Generalized DEA Model.” Journal of Econometries, 80, 63–84.

    Article  Google Scholar 

  • Zhu, J. (1996). “Data Envelopment Analysis with Preference Structure.” Journal of the Operational Research Society, 47, 136–150.

    Article  Google Scholar 

  • Zhu J. (2002). Quantitative Models for Performance Evaluation and Bench-Marking: DEA with Spreadsheets and DEA Excel Solver. Kluwer Academic Publishers, Boston.

    Google Scholar 

  • Zieschang, K. (1984). “An Extended Farrell Efficiency Measure.” Journal of Economic Theory, 33, 387–396.

    Article  Google Scholar 

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Correspondence to Wenbin Liu.

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Liu, W., Sharp, J. & Wu, Z. Preference, Production and Performance in Data Envelopment Analysis. Ann Oper Res 145, 105–127 (2006). https://doi.org/10.1007/s10479-006-0042-7

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  • DOI: https://doi.org/10.1007/s10479-006-0042-7

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