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Finding common weights based on the DM's preference information

Author

Listed:
  • G R Jahanshahloo

    (Islamic Azad University–Science and Research Branch)

  • M Zohrehbandian

    (Islamic Azad University–Karaj Branch)

  • A Alinezhad

    (Islamic Azad University–Qazvin Branch)

  • S Abbasian Naghneh

    (Islamic Azad University–Karaj Branch)

  • H Abbasian

    (Islamic Azad University–Karaj Branch)

  • R Kiani Mavi

    (Islamic Azad University–Qazvin Branch)

Abstract

Data Envelopment Analysis (DEA) is basically a linear programming-based technique used for measuring the relative performance of organizational units, referred to as Decision Making Units (DMUs). The flexibility in selecting the weights in standard DEA models deters the comparison among DMUs on a common base. Moreover, these weights are not suitable to measure the preferences of a decision maker (DM). For dealing with the first difficulty, the concept of common weights was proposed in the DEA literature. But, none of the common weights approaches address the second difficulty. This paper proposes an alternative approach that we term as ‘preference common weights’, which is both practical and intellectually consistent with the DEA philosophy. To do this, we introduce a multiple objective linear programming model in which objective functions are input/output variables subject to the constraints similar to the equations that define production possibility set of standard DEA models. Then by using the Zionts–Wallenius method, we can generate common weights as the DM's underlying value structure about objective functions.

Suggested Citation

  • G R Jahanshahloo & M Zohrehbandian & A Alinezhad & S Abbasian Naghneh & H Abbasian & R Kiani Mavi, 2011. "Finding common weights based on the DM's preference information," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(10), pages 1796-1800, October.
  • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:10:d:10.1057_jors.2010.156
    DOI: 10.1057/jors.2010.156
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    References listed on IDEAS

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    Cited by:

    1. Nam Hyok Kim & Feng He & Kwon Ryong Hong & Hyok-Chol Kim & Sok-Min Han, 2024. "A new common weights DEA model based on cluster analysis," Operational Research, Springer, vol. 24(2), pages 1-35, June.
    2. Qing Wang & Zhaojun Liu & Yang Zhang, 2017. "A Novel Weighting Method for Finding Common Weights in DEA," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-21, October.
    3. 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.
    4. Kiani Mavi, Reza & Kiani Mavi, Neda & Farzipoor Saen, Reza & Goh, Mark, 2022. "Common weights analysis of renewable energy efficiency of OECD countries," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    5. Kim, Nam Hyok & He, Feng & Kwon, O Chol, 2023. "Combining common-weights DEA window with the Malmquist index: A case of China’s iron and steel industry," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    6. I. Contreras & S. Lozano & M. A. Hinojosa, 2021. "A bargaining approach to determine common weights in DEA," Operational Research, Springer, vol. 21(3), pages 2181-2201, September.
    7. Dyckhoff, Harald & Souren, Rainer, 2022. "Integrating multiple criteria decision analysis and production theory for performance evaluation: Framework and review," European Journal of Operational Research, Elsevier, vol. 297(3), pages 795-816.
    8. Jie Wu & Junfei Chu & Qingyuan Zhu & Yongjun Li & Liang Liang, 2016. "Determining common weights in data envelopment analysis based on the satisfaction degree," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(12), pages 1446-1458, December.

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