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Restricting Weights in Value Efficiency Analysis


  • M. Halme
  • P. Korhonen


In this paper, we consider the problem of incorporating additional preference information into Value Efficiency Analysis by using the "price" information of inputs and outputs. This is done to improve the accuracy of the estimation of the Value Efficiency Scores. Value Efficiency developed by Halme et al (1998) is an efficiency concept, which takes into account the decision maker's preferences. Value Efficiency Analysis is based on the assumption that an explicitly known value function reaches its maximum at the Most Preferred Solution on the efficient frontier. The Most Preferred solution is an input- output vector preferred to all other possible input-output vectors. The ultimate goal is to measure a need to improve (radially) the values of inputs and/or outputs to make them equally preferred to the Most Preferred Solution. Because we do not know the value function, we approximate the indifference curves of all possible value functions satisfying certain assumptions by their tangents at the Most Preferred Solution. However, in addition to the Most preferred Solution information about the "prices" of inputs and outputs may be available as well. We show how this information can be incorporated into the analysis and illustrate the approach by an example on the performance of municipal dental units in Finland.

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  • M. Halme & P. Korhonen, 1998. "Restricting Weights in Value Efficiency Analysis," Working Papers ir98104, International Institute for Applied Systems Analysis.
  • Handle: RePEc:wop:iasawp:ir98104

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    References listed on IDEAS

    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. 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.
    3. R. Allen & A. Athanassopoulos & R.G. Dyson & E. Thanassoulis, 1997. "Weights restrictions and value judgements in Data Envelopment Analysis: Evolution, development and future directions," Annals of Operations Research, Springer, vol. 73(0), pages 13-34, October.
    4. P. Korhonen, 1998. "Multiple Objective Programming Support," Working Papers ir98010, International Institute for Applied Systems Analysis.
    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. E. Thanassoulis & R. Allen, 1998. "Simulating Weights Restrictions in Data Envelopment Analysis by Means of Unobserved DMUs," Management Science, INFORMS, vol. 44(4), pages 586-594, April.
    7. Merja Halme & Tarja Joro & Pekka Korhonen & Seppo Salo & Jyrki Wallenius, 1999. "A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis," Management Science, INFORMS, vol. 45(1), pages 103-115, January.
    8. Thanassoulis, E. & Dyson, R. G., 1992. "Estimating preferred target input-output levels using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 56(1), pages 80-97, January.
    9. M. Halme & T. Joro & P. Korhonen & S. Salo & J. Wallenius, 1998. "Value Efficiency Analysis for Incorporating Preference Information in Data Envelopment Analysis," Working Papers ir98054, International Institute for Applied Systems Analysis.
    10. 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.
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    3. Ahti Salo & Antti Punkka, 2011. "Ranking Intervals and Dominance Relations for Ratio-Based Efficiency Analysis," Management Science, INFORMS, vol. 57(1), pages 200-214, January.
    4. Podinovski, Victor V., 2016. "Optimal weights in DEA models with weight restrictions," European Journal of Operational Research, Elsevier, vol. 254(3), pages 916-924.
    5. Alcaide-López-de-Pablo, David & Dios-Palomares, Rafaela & Prieto, Ángel M., 2014. "A new multicriteria approach for the analysis of efficiency in the Spanish olive oil sector by modelling decision maker preferences," European Journal of Operational Research, Elsevier, vol. 234(1), pages 241-252.
    6. repec:pal:jorsoc:v:55:y:2004:i:8:d:10.1057_palgrave.jors.2601752 is not listed on IDEAS
    7. Frantisek Brazdik, 2005. "Oriented stochastic data envelopment models: Ranking comparison to stochastic frontier approach," CERGE-EI Working Papers wp271, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    8. repec:spr:grdene:v:14:y:2005:i:3:d:10.1007_s10726-005-6493-4 is not listed on IDEAS
    9. Dimitrov, Stanko & Sutton, Warren, 2010. "Promoting symmetric weight selection in data envelopment analysis: A penalty function approach," European Journal of Operational Research, Elsevier, vol. 200(1), pages 281-288, January.
    10. Hinojosa, M.A. & Mármol, A.M., 2011. "Axial solutions for multiple objective linear problems. An application to target setting in DEA models with preferences," Omega, Elsevier, vol. 39(2), pages 159-167, April.
    11. Maarit Kallio & Markku Kallio, 2002. "Nonparametric Methods for Evaluating Economic Efficiency and Imperfect Competition," Journal of Productivity Analysis, Springer, vol. 18(2), pages 171-189, September.
    12. Peter Bogetoft & Kurt Nielsen, 2005. "Internet Based Benchmarking," Group Decision and Negotiation, Springer, vol. 14(3), pages 195-215, May.
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