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Keywords = cloud–TODIM method

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24 pages, 14135 KB  
Article
Developing a Novel Robust Model to Improve the Accuracy of River Ecosystem Health Assessment in the Qinghai–Tibet Plateau
by Yuan Xu, Yun Li, Xiaogang Wang, Jianmin Zhang and Zhengxian Zhang
Sustainability 2025, 17(5), 2041; https://doi.org/10.3390/su17052041 - 27 Feb 2025
Viewed by 1062
Abstract
River ecosystem health assessment (REHA) is crucial for sustainable river management and water security. However, existing REHA methodologies still fail to consider the multiple effects of input uncertainty, environmental stochasticity, and the decision-maker’s bounded rationality. Moreover, REHA studies primarily focused on plain areas, [...] Read more.
River ecosystem health assessment (REHA) is crucial for sustainable river management and water security. However, existing REHA methodologies still fail to consider the multiple effects of input uncertainty, environmental stochasticity, and the decision-maker’s bounded rationality. Moreover, REHA studies primarily focused on plain areas, leaving the Qinghai–Tibet Plateau (QTP) understudied despite its ecosystems’ heightened fragility and complexity. To address these gaps, this study combined Pythagorean fuzzy sets with cloud modeling and proposed the Pythagorean fuzzy cloud (PFC) approach. Accordingly, a novel robust model (PFC-TODIM) was created by expanding the conventional TODIM method to the PFC algorithm. We provided an REHA indicator system tailored to the distinctive characteristics in the QTP, leveraging multisource data. River ecosystem health, driving mechanisms, and potential threats were investigated in the Lhasa River (LR) using the PFC-TODIM model. Results showed that the created model effectively took multiple uncertainties into consideration, thereby improving the REHA accuracy and robustness. In the LR, health conditions demonstrated substantial spatial disparities. Sampling sites of 28%, 48%, and 24% were subhealthy, healthy, and excellent, respectively. Findings showed that anthropogenic factors, such as dams, urban development, and fish release adversely affect river health and should be properly managed. Full article
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17 pages, 2616 KB  
Article
Extended Cloud–TODIM Method for Multiple-Attribute Decision-Making Problems in Risk Reduction Schemes: Application in the Tailings Storage Facility Failure
by Yusong Zhao and Congcong Chen
Appl. Sci. 2025, 15(4), 2091; https://doi.org/10.3390/app15042091 - 17 Feb 2025
Viewed by 720
Abstract
Various tailings storage facility (TSF) failures have caused catastrophic consequences, such as life and property losses and environmental destruction. It is crucial to select the optimal risk reduction scheme (RRS) to guarantee the safety and stability of the TSF. Decision-making problems in RRS [...] Read more.
Various tailings storage facility (TSF) failures have caused catastrophic consequences, such as life and property losses and environmental destruction. It is crucial to select the optimal risk reduction scheme (RRS) to guarantee the safety and stability of the TSF. Decision-making problems in RRS selection for TSF failure are multiple-attribute decision-making problems. During the RRS selection process, the psychological behavior of the decision makers should be considered. To solve such problems, the cloud–TODIM (abbreviation for interactive and multi-attribute decision-making in Portuguese) method is proposed for RRS selection in this paper. Firstly, the quantitative evaluation information is qualified and converted into clouds based on the cloud model, in which the characteristics of fuzziness, uncertainty, and randomness can be described. Secondly, an improved TODIM method is proposed to select the optimal RRS. Furthermore, a TSF is employed as a case study to examine the superiority of the proposed method. Finally, the sensitivity of the loss aversion coefficient θ, which reflects the attitudes of the decision makers (DMs) to the loss, is analyzed and a comparative analysis is developed therefore illustrating the competitiveness of the multiple-attribute decision-making of the proposed method. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in the Mining Industry)
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19 pages, 1078 KB  
Article
Robot Evaluation and Selection with Entropy-Based Combination Weighting and Cloud TODIM Approach
by Jing-Jing Wang, Zhong-Hua Miao, Feng-Bao Cui and Hu-Chen Liu
Entropy 2018, 20(5), 349; https://doi.org/10.3390/e20050349 - 7 May 2018
Cited by 46 | Viewed by 5748
Abstract
Nowadays robots have been commonly adopted in various manufacturing industries to improve product quality and productivity. The selection of the best robot to suit a specific production setting is a difficult decision making task for manufacturers because of the increase in complexity and [...] Read more.
Nowadays robots have been commonly adopted in various manufacturing industries to improve product quality and productivity. The selection of the best robot to suit a specific production setting is a difficult decision making task for manufacturers because of the increase in complexity and number of robot systems. In this paper, we explore two key issues of robot evaluation and selection: the representation of decision makers’ diversified assessments and the determination of the ranking of available robots. Specifically, a decision support model which utilizes cloud model and TODIM (an acronym in Portuguese of interactive and multiple criteria decision making) method is developed for the purpose of handling robot selection problems with hesitant linguistic information. Besides, we use an entropy-based combination weighting technique to estimate the weights of evaluation criteria. Finally, we illustrate the proposed cloud TODIM approach with a robot selection example for an automobile manufacturer, and further validate its effectiveness and benefits via a comparative analysis. The results show that the proposed robot selection model has some unique advantages, which is more realistic and flexible for robot selection under a complex and uncertain environment. Full article
(This article belongs to the Section Information Theory, Probability and Statistics)
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