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A Novel Z-TOPSIS Method Based on Improved Distance Measure of Z-Numbers

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Abstract

Decision-making is an activity based on human cognitive information. Z-numbers is a new concept that takes into account both the description of cognitive information and the reliability of the information. The distance measure of Z-numbers is an important subject in the decision-making process based on Z-information. However, the existing Z-number distance measure methods have problems of incomplete consideration and loss of information, which will lead to unreasonable results in decision-making. To this end, this paper proposes an improved distance measure of Z-numbers. The proposed method considers all aspects of the Z-number and preserves the original information as much as possible. It can overcome the shortcomings of existing methods. Some numerical examples are used to show the detailed calculation process and the advantages of the proposed method. To extend the classic Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) to the Z-information environment, we suggested the Z-TOPSIS method based on the improved distance measure of Z-numbers. An example of supplier selection indicates the feasibility and effectiveness of the proposed Z-TOPSIS method. Illustrative and comparative analyses further demonstrate the flexibility and superiority of the proposed method.

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Acknowledgements

The work is partially supported by the Fund of the National Natural Science Foundation of China (Grant No. 61903307), China Postdoctoral Science Foundation (Grant No. 2020M683575), Chinese Universities Scientific Fund (Grant No. 2452018066), Key R&D Program of Shaanxi Province, China (Grant No. 2019NY-164) and the National College Students Innovation and Entrepreneurship Training Program (Grant Nos. 202110712143, 202110712146).

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Authors and Affiliations

  1. College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China

    Ruolan Cheng, Jianfeng Zhang & Bingyi Kang

  2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, 712100, Shaanxi, China

    Bingyi Kang

  3. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, China

    Bingyi Kang

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  1. Ruolan Cheng
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  2. Jianfeng Zhang
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Correspondence to Bingyi Kang.

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The authors declare that they have no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors.

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Cheng, R., Zhang, J. & Kang, B. A Novel Z-TOPSIS Method Based on Improved Distance Measure of Z-Numbers. Int. J. Fuzzy Syst. 24, 2813–2830 (2022). https://doi.org/10.1007/s40815-022-01297-w

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