An Optimization-Based Approach to Social Network Group Decision Making with an Application to Earthquake Shelter-Site Selection

中心性 偏爱 一致性(知识库) 计算机科学 群体决策 选择(遗传算法) 应急管理 领域(数学) 运筹学 关系(数据库) 风险分析(工程) 管理科学 业务 工程类 人工智能 数据挖掘 微观经济学 心理学 经济 社会心理学 数学 统计 纯数学 经济增长
作者
Hengjie Zhang,Wang Fang,Huali Tang,Yucheng Dong
出处
期刊:International Journal of Environmental Research and Public Health [Multidisciplinary Digital Publishing Institute]
卷期号:16 (15): 2740-2740 被引量:2
标识
DOI:10.3390/ijerph16152740
摘要

The social network has emerged as an essential component in group decision making (GDM) problems. Thus, this paper investigates the social network GDM (SNGDM) problem and assumes that decision makers offer their preferences utilizing additive preference relations (also called fuzzy preference relations). An optimization-based approach is devised to generate the weights of decision makers by combining two reliable resources: in-degree centrality indexes and consistency indexes. Based on the obtained weights of decision makers, the individual additive preference relations are aggregated into a collective additive preference relation. Further, the alternatives are ranked from best to worst according to the obtained collective additive preference relation. Moreover, earthquakes have occurred frequently around the world in recent years, causing great loss of life and property. Earthquake shelters offer safety, security, climate protection, and resistance to disease and ill health and are thus vital for disaster-affected people. Selection of a suitable site for locating shelters from potential alternatives is of critical importance, which can be seen as a GDM problem. When selecting a suitable earthquake shelter-site, the social trust relationships among disaster management experts should not be ignored. To this end, the proposed SNGDM model is applied to evaluate and select earthquake shelter-sites to show its effectiveness. In summary, this paper constructs a novel GDM framework by taking the social trust relationship into account, which can provide a scientific basis for public emergency management in the major disasters field.
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