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The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems

Author

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  • Peng Wang

    (Northwestern Polytechnical University)

  • Zhouquan Zhu

    (Northwestern Polytechnical University)

  • Shuai Huang

    (Northwestern Polytechnical University)

Abstract

Decision makers today are faced with a wide range of alternative options and a large set of conflicting criteria. How to make trade-off between these conflicting attributes and make a scientific decision is always a difficult task. Although a lot of multiple criteria decision making (MCDM) methods are available to deal with selection applications, it’s observed that in most of these methods the ranking results are very sensitive to the changes in the attribute weights. The calculation process is also ineffective when a new alternative is added or removed from the MCDM problem. This paper presents an improved TOPSIS method based on experimental design and Chebyshev orthogonal polynomial regression. A feature of this method is that it employs the experimental design technique to assign the attribute weights and uses Chebyshev regression to build a regression model. This model can help and guide a decision maker to make a reasonable judgment easily. The proposed methodology is particularized through an equipment selection problem in manufacturing environment. Two more illustrative examples are conducted to demonstrate the applicability of the proposed method. In all the cases, the results obtained using the proposed method almost corroborate with those derived by the earlier researchers which proves the validity, capability and potentiality of this method in solving real-life MCDM problems.

Suggested Citation

  • Peng Wang & Zhouquan Zhu & Shuai Huang, 2017. "The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 229-243, January.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0973-9
    DOI: 10.1007/s10845-014-0973-9
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    References listed on IDEAS

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    Cited by:

    1. Hyunseop Park & Hyunwoong Ko & Yung-tsun Tina Lee & Shaw Feng & Paul Witherell & Hyunbo Cho, 2023. "Collaborative knowledge management to identify data analytics opportunities in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 541-564, February.
    2. Vadivel Sengazhani Murugesan & A. H. Sequeira & Deeksha Sanjay Shetty & Sunil Kumar Jauhar, 2020. "Enhancement of mail operational performance of India post facility layout using AHP," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 261-273, April.
    3. Kumar, S. Charles Ruskin & Anuradha, R. & Dharmaraj, R., 2021. "Numerical investigation on ultra-high performance concrete beam under pure bending," Resources Policy, Elsevier, vol. 74(C).

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