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Determining attribute weights using mathematical programming

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  • Shirland, Larry E.
  • Jesse, Richard R.
  • Thompson, Ronald L.
  • Iacovou, Charalambos L.

Abstract

Group decisions are an important element of successful knowledge management in organizations. Such decisions are difficult to make, however, especially when they involve a large set of attributes that require decision-makers to develop rankings. This paper presents a goal programming model for determining constrained regression estimates of attribute weights. The model is developed using pair-wise comparison ratings that are derived by using triads of the attributes. In addition, metrics are presented for measuring individual and group consensus. A specific application to the health care industry is presented to illustrate results that are obtained from the model.

Suggested Citation

  • Shirland, Larry E. & Jesse, Richard R. & Thompson, Ronald L. & Iacovou, Charalambos L., 2003. "Determining attribute weights using mathematical programming," Omega, Elsevier, vol. 31(6), pages 423-437, December.
    • Handle: RePEc:eee:jomega:v:31:y:2003:i:6:p:423-437
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    References listed on IDEAS

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    Cited by:

    1. Jessop, Alan, 2014. "IMP: A decision aid for multiattribute evaluation using imprecise weight estimates," Omega, Elsevier, vol. 49(C), pages 18-29.
    2. Zeng, Shouzhen & Streimikiene, Dalia & Baležentis, Tomas, 2017. "Review of and comparative assessment of energy security in Baltic States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 185-192.
    3. József Temesi, 2011. "Pairwise comparison matrices and the error-free property of the decision maker," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(2), pages 239-249, June.
    4. Parishani, Maede & Rasti-Barzoki, Morteza, 2024. "CWBCM method to determine the importance of classification performance evaluation criteria in machine learning: Case studies of COVID-19, Diabetes, and Thyroid Disease," Omega, Elsevier, vol. 127(C).
    5. Chao Fu & Dong-Ling Xu, 2016. "Determining attribute weights to improve solution reliability and its application to selecting leading industries," Annals of Operations Research, Springer, vol. 245(1), pages 401-426, October.
    6. Dong, Yucheng & Liu, Yating & Liang, Haiming & Chiclana, Francisco & Herrera-Viedma, Enrique, 2018. "Strategic weight manipulation in multiple attribute decision making," Omega, Elsevier, vol. 75(C), pages 154-164.
    7. Yang, Guo-liang & Yang, Jian-Bo & Xu, Dong-Ling & Khoveyni, Mohammad, 2017. "A three-stage hybrid approach for weight assignment in MADM," Omega, Elsevier, vol. 71(C), pages 93-105.
    8. Zhang, Zhi-Gang & Hu, Xiao & Liu, Zhao-Ting & Zhao, Lu-Tao, 2021. "Multi-attribute decision making: An innovative method based on the dynamic credibility of experts," Applied Mathematics and Computation, Elsevier, vol. 393(C).
    9. Doan, N.A.V. & De Smet, Y., 2018. "An alternative weight sensitivity analysis for PROMETHEE II rankings," Omega, Elsevier, vol. 80(C), pages 166-174.

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