A clustering-based method for large-scale group decision making in the hesitant fuzzy set environment

Han, Yuxing; Xu, Gaili; Wang, Feng; Zhang, Yunfei

doi:10.1016/j.cie.2023.109526

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…In recent years, due to the complexity and uncertainty of objective things as well as the ambiguity of human thinking, the study of uncertain multiple-attribute decisionmaking problems has attracted extensive attention from the academic community [36]. Multiple-attribute decision-making has also evolved to encompass fuzzy clustering, gray fuzzy comprehensive evaluation, and innovative clustering methods [37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Automatic Group Decision-Making for Algal Bloom Management Based on Information Self-Learning

Bai,

Niu,

Zhao

et al. 2024

Water

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The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms.

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