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Relative Ratio Method For Multiple Attribute Decision Making Problems

Author

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  • DENG-FENG LI

    (Department of Sciences, Shenyang Institute of Aeronautical Engineering, Shenyang 110034, Liaoning, China;
    Department Five, Dalian Naval Academy, No. 1, Xiaolong Street, Dalian 116018, Liaoning, China)

Abstract

The multiple attribute decision making (MADM) is an important research field in decision science and operations research. Recently, several commonly used methods such as the TOPSIS and the VIKOR were proposed to solve the MADM problems. The TOPSIS and VIKOR are based on aggregating functions representing closeness to the ideal, which originated in the compromise programming method. The aim of this paper is to develop a new methodology called the relative ratio (RR) for the MADM problems. In this RR method, a compromise solution/alternative is determined based on the concept that the chosen alternative should be as close to the ideal solution as possible and as far away from the negative-ideal solution as possible simultaneously. The computation principle and procedure of the RR method are described in detail in this paper. Moreover comparisons of the RR method with the TOPSIS as well as the VIKOR are made theoretically and illustrated with a numerical example.

Suggested Citation

  • Deng-Feng Li, 2009. "Relative Ratio Method For Multiple Attribute Decision Making Problems," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 289-311.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:02:n:s0219622009003405
    DOI: 10.1142/S0219622009003405
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    Cited by:

    1. Kuo, Ting, 2017. "A modified TOPSIS with a different ranking index," European Journal of Operational Research, Elsevier, vol. 260(1), pages 152-160.
    2. Michael C. Nwogugu, 2020. "Decision-Making, Sub-Additive Recursive "Matching" Noise And Biases In Risk-Weighted Stock/Bond Index Calculation Methods In Incomplete Markets With Partially Observable Multi-Attribute Pref," Papers 2005.01708, arXiv.org.
    3. Francesco Ciardiello & Andrea Genovese, 2023. "A comparison between TOPSIS and SAW methods," Annals of Operations Research, Springer, vol. 325(2), pages 967-994, June.
    4. Jiamin Wang, 2012. "Robust optimization analysis for multiple attribute decision making problems with imprecise information," Annals of Operations Research, Springer, vol. 197(1), pages 109-122, August.
    5. Deng-Feng Li & Shu-Ping Wan, 2017. "Minimum Weighted Minkowski Distance Power Models for Intuitionistic Fuzzy Madm with Incomplete Weight Information," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1387-1408, September.
    6. Susmaga, Robert & Szczȩch, Izabela & Zielniewicz, Piotr & Brzezinski, Dariusz, 2023. "MSD-space: Visualizing the inner-workings of TOPSIS aggregations," European Journal of Operational Research, Elsevier, vol. 308(1), pages 229-242.

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