Yazar "Chin, Kwai-Sang" seçeneğine göre listele

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    Enhancing the sustainability and robustness of critical material supply in electrical vehicle market: an AI-powered supplier selection approach
    (Springer, 2023) Wang, Zhu-Jun; Chen, Zhen-Song; Su, Qin; Chin, Kwai-Sang; Pedrycz, Witold; Skibniewski, Miroslaw J.
    In light of the burgeoning electric vehicle market, the demand for lithium-ion batteries (LiBs) is on the rise. However, the supply of materials essential for LiBs is struggling to keep pace, posing a significant challenge in meeting the surging market demand. This study offers a viable solution to bolster the dependability of the material supply chain by prioritizing material suppliers who are deeply committed to sustainable practices and performance. We have developed a comprehensive system for evaluating sustainable performance, encompassing three vital dimensions: economic, social and environmental contexts. Then, we introduced a pioneering approach known as the multi-criteria material supplier selection (MCMSS) methodology which amalgamates multi-criteria decision-making techniques with artificial intelligence to effectively generate sustainability performance of suppliers and identify the most suitable supplier, out of all alternatives. Eventually, the supply of four key materials of LiBs is used as illustrative examples to verify the feasibility and rationality of the proposed MCMSS. This work carries significant implications for overseeing the LiB material industry. The MCMSS model offers a solution for the government to establish a comprehensive material supplier database to intelligently supervise the activities of material suppliers and foster collaboration between upstream and downstream enterprises within the LiB industry.
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    NONPARAMETRIC NUMERICAL APPROACHES TO PROBABILITY WEIGHTING FUNCTION CONSTRUCTION FOR MANIFESTATION AND PREDICTION OF RISK PREFERENCES
    (Vilnius Gediminas Tech Univ, 2023) Wu, Sheng; Chen, Zhen-Song; Pedrycz, Witold; Govindan, Kannan; Chin, Kwai-Sang
    Probability weighting function (PWF) is the psychological probability of a decision-maker for ob-jective probability, which reflects and predicts the risk preferences of decision-maker in behavioral decision-making. The existing approaches to PWF estimation generally include parametric methodologies to PWF con-struction and nonparametric elicitation of PWF. However, few of them explores the combination of parametric and nonparametric elicitation approaches to approximate PWF. To describe quantitatively risk preferences, the Newton interpolation, as a well-established mathematical approximation approach, is introduced to task-specifi-cally match PWF under the frameworks of prospect theory and cumulative prospect theory with descriptive psy-chological analyses. The Newton interpolation serves as a nonparametric numerical approach to the estimation of PWF by fitting experimental preference points without imposing any specific parametric form assumptions. The elaborated nonparametric PWF model varies in accordance with the number of the experimental preference points elicitation in terms of its functional form. The introduction of Newton interpolation to PWF estimation into decision-making under risk will benefit to reflect and predict the risk preferences of decision-makers both at the aggregate and individual levels. The Newton interpolation-based nonparametric PWF model exhibits an inverse S-shaped PWF and obeys the fourfold pattern of decision-makers' risk preferences as suggested by previous empirical analyses.
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    Optimized decision support for BIM maturity assessment
    (Elsevier, 2023) Chen, Zhen-Song; Zhou, Meng-Die; Chin, Kwai-Sang; Darko, Amos; Wang, Xian-Jia; Pedrycz, Witold
    Building information modeling (BIM) maturity models occupy a crucial role in guiding BIM-reliant stakeholders and enterprises to identify BIM capabilities and facilitate process improvements. Nevertheless, few quantitative BIM maturity models are available for the measurement and improvement of BIM utilization performance. This study designs a refined assessment system for the maturity measurement of BIM-based projects during the design and construction stages. The advocated BIM maturity model combines a probability distribution function aggregation paradigm and a large-scale group decision-making framework to provide an expert-based assessment system for evaluating project-based BIM performance. The case study of the Corning Gen 10.5 glass substrate production line workshop in Wuhan demonstrates the feasibility and effectiveness of the proposed model. This paper establishes a generalizable structural framework that can potentially facilitate BIM maturity analysis in a portfolio of projects or the industry as a whole and will generate fresh insight into designing quantitative BIM maturity models across various contexts.