基于互动迭代共识的社会网络大规模群体决策方法及其在零废弃物城市评估中的应用

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Information Fusion Pub Date : 2024-10-16 DOI:10.1016/j.inffus.2024.102744
Fanyong Meng , Hao Li , Jinyu Li
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引用次数: 0

摘要

零废弃物(ZW)城市的建设越来越受到中国政府的重视。要根据各地的实际情况进行政策调整,评估必不可少。以往的零废弃城市评估主要依赖于历史数据,未能考虑各利益相关方的主观偏好。例如,捕捉居民对 ZW 城市发展的主观意见是一项挑战。本文提出了一种评估 ZW 城市建设的社会网络大规模群体决策方法。首先,利用专家的评价意见和信任关系开发了一种改进的聚类方法。然后利用内外部凝聚力指数和专家人数确定聚类的权重,专家权重由其相似度-信任度定义。考虑到分配方案的公平性和合理性,建立了基于交互迭代共识的优化模型。此外,还提出了一种新的社会网络大规模群体决策方法。最后,以江苏省选择国家级 ZW 城市的案例研究说明了所提出的方法。
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An interactive iteration consensus based social network large-scale group decision making method and its application in zero-waste city evaluation
The construction of zero-waste (ZW) cities receives increasing attention from the Chinese government. The evaluation is essential to make policy variations according to the actual situation in each place. Previous assessments of ZW cities have primarily relied on historical data, which fails to account for the subjective preferences of various stakeholders. For example, it is challenging to capture residents' subjective opinions about the development of a ZW city. This paper presents a social network large-scale group decision-making method for evaluating the construction of ZW city. First, experts' evaluation opinions and trust relations are used to develop an improved clustering method. The weights of the clusters are then determined using internal-external cohesion indices and the number of experts, with experts' weights defined by their similarity-trust degree. An optimization model based on interactive iteration consensus is formulated, considering the fairness and rationality of allocation schemes. Additionally, a new social network large-scale group decision-making method is presented. Finally, the proposed method is illustrated with a case study of selecting a national-level ZW city in Jiangsu Province.
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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