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A Probabilistic Linguistic Multiple Attribute Decision Making Based on a New Correlation Coefficient Method and its Application in Hospital Assessment

Author

Listed:
  • Dandan Luo

    (School of Business, Ningbo University, Ningbo 315211, China)

  • Shouzhen Zeng

    (School of Business, Ningbo University, Ningbo 315211, China
    College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China
    School of Management, Fudan University, Shanghai 200433, China)

  • Ji Chen

    (College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China)

Abstract

The probabilistic linguistic term set (PLTS) is a newly emerging mathematical tool for handling uncertainties. It is considered a useful extension of linguistic term sets associated with probability information and can improve the effectiveness of multiple attribute decision making (MADM). This paper proposes a new PLTS correlation coefficient and addresses its usefulness in MADM problems. For achieving this aim, some new concepts of mean, variance, and covariance of the PLTS are first proposed. Moreover, a novel PLTS Pearson correlation coefficient is defined to overcome the shortcomings of the existing methods, whose significant feature is that it lies in the interval [−1,1], which makes it more effective in reflecting the negative and positive correlation between PLTSs. A weighted PLTS Pearson correlation coefficient is further defined to consider the importance of attribute weights and expand the scope of application. Then, a relative PLTS closeness coefficient is constructed based on the developed Pearson correlation coefficient, and based on which, a Pearson correlation-based TOPSIS (technique for order of preference by similarity to ideal solution) approach for MADM problems is developed. Finally, the effectiveness as well as the applicability of the developed method are illustrated through numerical examples and comparative analysis.

Suggested Citation

  • Dandan Luo & Shouzhen Zeng & Ji Chen, 2020. "A Probabilistic Linguistic Multiple Attribute Decision Making Based on a New Correlation Coefficient Method and its Application in Hospital Assessment," Mathematics, MDPI, vol. 8(3), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:3:p:340-:d:328113
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    References listed on IDEAS

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    1. Yun Jin & Hecheng Wu & Dechao Sun & Shouzhen Zeng & Dandan Luo & Bo Peng, 2019. "A Multi-Attribute Pearson’s Picture Fuzzy Correlation-Based Decision-Making Method," Mathematics, MDPI, vol. 7(10), pages 1-12, October.
    2. Shouzhen Zeng & Dandan Luo & Chonghui Zhang & Xingsen Li, 2020. "A Correlation-Based TOPSIS Method for Multiple Attribute Decision Making with Single-Valued Neutrosophic Information," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 343-358, February.
    3. Xiaolu Zhang & Xiaoming Xing, 2017. "Probabilistic Linguistic VIKOR Method to Evaluate Green Supply Chain Initiatives," Sustainability, MDPI, vol. 9(7), pages 1-18, July.
    4. Shouzhen Zeng & Jianping Chen & Xingsen Li, 2016. "A Hybrid Method for Pythagorean Fuzzy Multiple-Criteria Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 403-422, March.
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    Cited by:

    1. Chonghui Zhang & Weihua Su & Sichao Chen & Shouzhen Zeng & Huchang Liao, 2023. "A Combined Weighting Based Large Scale Group Decision Making Framework for MOOC Group Recommendation," Group Decision and Negotiation, Springer, vol. 32(3), pages 537-567, June.

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