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A note on article "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees"

Author

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  • Gao, Bin
  • Ni, Ming-Fang

Abstract

Wang et al. use an evidential reasoning approach for solving multiple attribute decision analysis (MADA) problems under interval belief degrees [Y.M. Wang, J.B. Yang, D.L. Xu, K.S. Chin, The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees, European Journal of Operational Research 175 (2006) 35-66]. In this note it is shown some nonlinear optimization models in that paper are incorrect. The necessary corrections are proposed.

Suggested Citation

  • Gao, Bin & Ni, Ming-Fang, 2009. "A note on article "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees"," European Journal of Operational Research, Elsevier, vol. 197(2), pages 809-812, September.
    • Handle: RePEc:eee:ejores:v:197:y:2009:i:2:p:809-812
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    References listed on IDEAS

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    1. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling & Chin, Kwai-Sang, 2006. "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees," European Journal of Operational Research, Elsevier, vol. 175(1), pages 35-66, November.
    2. Xu, Dong-Ling & Yang, Jian-Bo & Wang, Ying-Ming, 2006. "The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1914-1943, November.
    3. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling, 2006. "Environmental impact assessment using the evidential reasoning approach," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1885-1913, November.
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    Cited by:

    1. Wang, Ying-Ming, 2009. "Reply to the note on article "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees"," European Journal of Operational Research, Elsevier, vol. 197(2), pages 813-817, September.

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