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Simulating Weights Restrictions in Data Envelopment Analysis by Means of Unobserved DMUs

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Author Info

  • E. Thanassoulis

    (Warwick Business School, Warwick University, Coventry, CV4 7AL, United Kingdom)

  • R. Allen

    (Warwick Business School, Warwick University, Coventry, CV4 7AL, United Kingdom)

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    Abstract

    Data envelopment analysis (DEA) is a method for assessing the comparative efficiencies of decision making units (e.g., banks and schools) by relating their output to their input levels. Restrictions are often imposed in these assessments to reflect prior judgments on the values of input and/or output variables. This paper introduces a new approach to capturing and using value judgments in DEA, based on unobserved Decision Making Units. In so doing, it opens up a whole new approach for reflecting value judgments in DEA assessments, which can offer advantages in certain situations.

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    File URL: http://dx.doi.org/10.1287/mnsc.44.4.586
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 44 (1998)
    Issue (Month): 4 (April)
    Pages: 586-594

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    Handle: RePEc:inm:ormnsc:v:44:y:1998:i:4:p:586-594

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    Related research

    Keywords: Data Envelopment Analysis; Weights Restrictions; Comparative Efficiency; Value Judgments; Unobserved Decision Making Units;

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    Cited by:
    1. Bougnol, M.-L. & Dulá, J.H., 2009. "Anchor points in DEA," European Journal of Operational Research, Elsevier, vol. 192(2), pages 668-676, January.
    2. Allen, R. & Thanassoulis, E., 2004. "Improving envelopment in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 363-379, April.
    3. Ghasemi, M.-R. & Ignatius, Joshua & Emrouznejad, Ali, 2014. "A bi-objective weighted model for improving the discrimination power in MCDEA," European Journal of Operational Research, Elsevier, vol. 233(3), pages 640-650.
    4. Victor Podinovski & Emmanuel Thanassoulis, 2007. "Improving discrimination in data envelopment analysis: some practical suggestions," Journal of Productivity Analysis, Springer, vol. 28(1), pages 117-126, October.
    5. Thanassoulis, Emmanuel & Kortelainen, Mika & Allen, Rachel, 2012. "Improving envelopment in Data Envelopment Analysis under variable returns to scale," European Journal of Operational Research, Elsevier, vol. 218(1), pages 175-185.
    6. Sowlati, Taraneh & Paradi, Joseph C., 2004. "Establishing the "practical frontier" in data envelopment analysis," Omega, Elsevier, vol. 32(4), pages 261-272, August.
    7. Chun-Chu Liu, 2006. "Simulating weight restrictions in data envelopment analysis using the subjective and objective integrated approach," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2545-2552.
    8. Jens Leth Hougaard & Mich Tvede, 2001. "Benchmark Selection: An Axiomatic Approach," Discussion Papers 01-06, University of Copenhagen. Department of Economics.
    9. HOSSEINZADEH LOTFI, Farhad & HATAMI-MARBINI, Adel & AGRELL, Per & GHOLAMI, Kobra, 2013. "Centralized resource reduction and target setting under DEA control," CORE Discussion Papers 2013005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Cherchye, Laurens & Kuosmanen, Timo & Post, Thierry, 2002. "Non-parametric production analysis in non-competitive environments," International Journal of Production Economics, Elsevier, vol. 80(3), pages 279-294, December.
    11. Norman Keith Womer & Homee Shroff & Thomas Gulledge & Kingsley Haynes, 2003. "Measuring efficiency with a linear economic model," Applied Economics, Taylor & Francis Journals, vol. 35(13), pages 1459-1467.
    12. Luo, Xueming & Donthu, Naveen, 2005. "Assessing advertising media spending inefficiencies in generating sales," Journal of Business Research, Elsevier, vol. 58(1), pages 28-36, January.
    13. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    14. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
    15. Johnston, Katharine & Gerard, Karen, 2001. "Assessing efficiency in the UK breast screening programme: does size of screening unit make a difference?," Health Policy, Elsevier, vol. 56(1), pages 21-32, April.
    16. Soteriou, Andreas C. & Zenios, Stavros A., 1999. "Using data envelopment analysis for costing bank products," European Journal of Operational Research, Elsevier, vol. 114(2), pages 234-248, April.
    17. Baris Yilmaz & Mehmet Yurdusev & Nilgun Harmancioglu, 2009. "The Assessment of Irrigation Efficiency in Buyuk Menderes Basin," Water Resources Management, Springer, vol. 23(6), pages 1081-1095, April.
    18. Talluri, Srinivas & Narasimhan, Ram, 2004. "A methodology for strategic sourcing," European Journal of Operational Research, Elsevier, vol. 154(1), pages 236-250, April.
    19. M. Halme & P. Korhonen, 1998. "Restricting Weights in Value Efficiency Analysis," Working Papers ir98104, International Institute for Applied Systems Analysis.
    20. Korhonen, Pekka & Tainio, Risto & Wallenius, Jyrki, 2001. "Value efficiency analysis of academic research," European Journal of Operational Research, Elsevier, vol. 130(1), pages 121-132, April.
    21. Krivonozhko, Vladimir E. & Førsund, Finn R. & Lychev, Andrey V., 2012. "Identifying Suspicious Efficient Units in DEA Models," Memorandum 30/2012, Oslo University, Department of Economics.
    22. Ahn, Heinz & Neumann, Ludmila & Vazquez Novoa, Nadia, 2012. "Measuring the relative balance of DMUs," European Journal of Operational Research, Elsevier, vol. 221(2), pages 417-423.

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