Yazar "Jin, LeSheng" seçeneğine göre listele

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    Generalized extended Bonferroni means for isomorphic membership grades
    (Elsevier B.V., 2024) Chen, Zhen Song; Yang, Yi; Jin, LeSheng; Dutta, Bapi; Martínez, Luis; Pedrycz, Witold; Mesiar, Radko; Bustince, Humberto
    The generalized extended Bonferroni mean (GEBM) is a powerful tool for modeling the complex process of aggregating information, whether it is homogeneously or heterogeneously connected, within a composite aggregation structure. It maintains several favorable characteristics and effectively captures the diverse and interconnected nature of expert opinions or criteria, which is commonly observed in various decision-making contexts. This research expands upon the existing GEBM framework by applying it to the specific domains of q-rung orthopair fuzzy sets (q-ROFSs) and extended q-rung orthopair fuzzy sets (Eq-ROFSs). Furthermore, it examines the transformation processes among different variants of GEBMs. To facilitate the development of generalized aggregation functions, the de Morgan triplets for q-ROFSs and Eq-ROFSs are established. By introducing an isomorphism, the transformation relationship between the aggregation functions for q-ROFSs and Eq-ROFSs is analyzed. Based on this foundation, the Bonferroni mean de Morgan triplet-based GEBMs for q-ROFSs and Eq-ROFSs are proposed, and the keeping-order relations for these proposed GEBMs are discussed. Finally, several special cases of the GEBMs for q-ROFSs and Eq-ROFSs are obtained, and several relevant theorems are verified. © 2024 Elsevier B.V.
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    Large-group failure mode and effects analysis for risk management of angle grinders in the construction industry
    (Elsevier, 2023) Chen, Zhen-Song; Chen, Jun-Yang; Chen, Yue-Hua; Yang, Yi; Jin, LeSheng; Herrera-Viedma, Enrique; Pedrycz, Witold
    Accidents associated with the use of construction equipment are among the leading causes of fatal injuries in the construction industry. In particular, angle grinders are associated with a significant number of occupational injuries every year. However, practitioners and researchers have paid limited attention to this issue. To facilitate the development of more sophisticated plans and guidelines to prevent angle grinder-related accidents, failure mode and effects analysis (FMEA) is employed for risk management in such a context. The conventional FMEA method is extensively used for examining potential failure in many industries, but has been criticized much in the literature for its various limitations. This study presents a novel large-group FMEA (LGFMEA) model, which integrates a clustering analysis for handling experts at large scale, a consensus reaching process with relative basic uncertain linguistic information (RBULI) to manage opinion diversity among experts, and the behavioral TOPSIS method to rank failure modes. The assessment information is characterized by the RBULI, a novel information representation construction method applied to handle complex evaluations under uncertainty. Finally, the proposed LGFMEA approach is performed for risk analysis related to angle grinder use to obtain insights into risk mitigation, and the sensitive and comparative analyses are performed to verify the rationality and feasibility of the model.