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Restricting weights in value efficiency analysis

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  • Halme, Merja
  • Korhonen, Pekka

Abstract

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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  • Halme, Merja & Korhonen, Pekka, 2000. "Restricting weights in value efficiency analysis," European Journal of Operational Research, Elsevier, vol. 126(1), pages 175-188, October.
  • Handle: RePEc:eee:ejores:v:126:y:2000:i:1:p:175-188
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    3. Podinovski, Victor V., 2016. "Optimal weights in DEA models with weight restrictions," European Journal of Operational Research, Elsevier, vol. 254(3), pages 916-924.
    4. Pereira, Miguel Alves & Camanho, Ana Santos & Figueira, José Rui & Marques, Rui Cunha, 2021. "Incorporating preference information in a range directional composite indicator: The case of Portuguese public hospitals," European Journal of Operational Research, Elsevier, vol. 294(2), pages 633-650.
    5. T Joro & E-J Viitala, 2004. "Weight-restricted DEA in action: from expert opinions to mathematical models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 814-821, August.
    6. Peter Bogetoft & Kurt Nielsen, 2005. "Internet Based Benchmarking," Group Decision and Negotiation, Springer, vol. 14(3), pages 195-215, May.
    7. 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.
    8. Henriques, C.O. & Marcenaro-Gutierrez, O.D., 2021. "Efficiency of secondary schools in Portugal: A novel DEA hybrid approach," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    9. Panagiotis Ravanos & Giannis Karagiannis, 2023. "On VEA, production trade-offs and weights restrictions," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(10), pages 2081-2093, October.
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    11. Panagiotis Ravanos & Giannis Karagiannis, 2021. "Using VEA to assess effectiveness in the development of human capabilities," Economic Change and Restructuring, Springer, vol. 54(1), pages 75-99, February.
    12. V V Podinovski, 2004. "Production trade-offs and weight restrictions in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1311-1322, December.
    13. Hosein Arman & Abdollah Hadi‐Vencheh, 2021. "Restricting the relative weights in data envelopment analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4127-4136, July.
    14. 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.
    15. Giannis Karagiannis & Panagiotis Ravanos, 2023. "On Value Efficiency Analysis and Cone-Ratio Data Envelopment Analysis models," Discussion Paper Series 2023_03, Department of Economics, University of Macedonia, revised Mar 2023.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. See, Kok Fong & Ng, Ying Chu & Yu, Ming-Miin, 2022. "An alternative assessment approach to national higher education system evaluation," Evaluation and Program Planning, Elsevier, vol. 94(C).
    21. Victor V. Podinovski & Tatiana Bouzdine-Chameeva, 2013. "Weight Restrictions and Free Production in Data Envelopment Analysis," Operations Research, INFORMS, vol. 61(2), pages 426-437, April.
    22. C Kao & H-T Hung, 2005. "Data envelopment analysis with common weights: the compromise solution approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(10), pages 1196-1203, October.
    23. Gerami, Javad & Mozaffari, Mohammad Reza & Wanke, Peter F. & Correa, Henrique L., 2022. "Improving information reliability of non-radial value efficiency analysis: An additive slacks based measure approach," European Journal of Operational Research, Elsevier, vol. 298(3), pages 967-978.

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