首页|New Findings on Intelligent Systems Described by Investigators at Shanxi Univers ity (A Spherical Z-number Multi-attribute Group Decision Making Model Based On t he Prospect Theory and Glds Method)
New Findings on Intelligent Systems Described by Investigators at Shanxi Univers ity (A Spherical Z-number Multi-attribute Group Decision Making Model Based On t he Prospect Theory and Glds Method)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning - Intelligent Systems. According to news reporting out of Taiyu an, People's Republic of China, by NewsRx editors, research stated, "Multi-attri bute group decision-making is an important research field in decision science, a nd its theories and methods have been widely applied to engineering, economics a nd management. However, as the information embedded volume and complexity of dec ision-making expand, the diversity and heterogeneity of decision-making groups p resent significant challenges to the decision-making process." Our news journalists obtained a quote from the research from Shanxi University, "In order to effectively address these challenges, this paper defines the concep t of spherical Z-number, which is a fuzzy number that takes into account a wide range of evaluation and its reliability. Additionally, a group decision-making m odel in a spherical Z-number environment is proposed. First, an objective phased tracking method is used to determine expert weights, maintain the consistency i n decision-making group evaluations. The gained and lost dominance score method is combined with prospect theory to integrate expert psychological behavior when facing risks. The proposed method considers both group utility and individual r egret, and balances the gains and losses of various options in the decision-maki ng process. Finally, in response to the 3R principle, the model is employed to a ddress the shared e-bike recycling supplier selection problem and to assess the viability of the decision-making outcomes. The results demonstrate that the mode l is robust in the context of varying parameter configurations. Moreover, the co rrelation coefficients between its ranking outcomes and those of alternative met hodologies are all above 0.77, and its average superiority degree is 1.121, whic h is considerably higher than that of other methods."
TaiyuanPeople's Republic of ChinaAsi aIntelligent SystemsMachine LearningShanxi University
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