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A hybrid multi-criteria decision making approach for assessing health-care waste management technologies based on soft likelihood function and D-numbers

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Abstract

Health-care waste (HCW) management is an important issue, especially in developing countries. How to choose the best management technology is a challenging and open subject in this issue. Limited work has been done, but there is still a lack of a technical approach that not only takes into account multi-granular linguistic terminology, but also considers the attitude characters of decision makers (DMs). To adress the above problem, in this paper a hybrid multi-criteria decision making scheme is proposed based on soft likelihood function and D-numbers. First, the D-numbers is used to characterize complex multi-grained decision information. Secondly, a novel soft likelihood function based on power ordered weighted averaging operator (POWA) is designed to effectively take into account the DMs’ preferences, which is then integrated into the proposed HCW management approach. Eventually, the effectiveness and superiority of the proposed approach is demonstrated through an application example. In particular, an intuitive advantage has been confirmed that the proposed method can adjust the gap between adjacent alternatives through decision preference to distinguish differences. This is expected to provide a reliable fault-tolerant interval for decision-making in HCW management, and further improve the reliability of the algorithm. In addition, the analysis and evaluation also confirm the reliability and practicality of the proposed technology.

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Acknowledgments

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),the Startup Fund from Northwest A&F University (Grant No. 2452018066), and the National College Students Innovation and Entrepreneurship Training Program (Grant No. S202010712135, No. S202010712019, No. X202010712364).

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Authors and Affiliations

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

    Xiangjun Mi, Ye Tian & Bingyi Kang

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

    Bingyi Kang

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

    Bingyi Kang

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  1. Xiangjun Mi
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Correspondence to Bingyi Kang.

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Mi, X., Tian, Y. & Kang, B. A hybrid multi-criteria decision making approach for assessing health-care waste management technologies based on soft likelihood function and D-numbers. Appl Intell 51, 6708–6727 (2021). https://doi.org/10.1007/s10489-020-02148-7

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