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基于非可加测度和前景理论的三支决策方法及其在航空装备风险分析中的应用研究

Research on a Three-Way Decision-Making Approach, Based on Non-Additive Measurement and Prospect Theory, and Its Application in Aviation Equipment Risk Analysis.

作者信息

Xia Ruicong, Tong Sirong, Wang Qiang, Sun Bingzhen, Xu Ziling, Liu Qiuhan, Yu Jiayang, Wu Fan

机构信息

Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi'an 710051, China.

School of Economics and Management, Xidian University, Xi'an 710071, China.

出版信息

Entropy (Basel). 2024 Jul 14;26(7):598. doi: 10.3390/e26070598.

Abstract

Due to the information non-independence of attributes, combined with a complex and changeable environment, the analysis of risks faces great difficulties. In view of this problem, this paper proposes a new three-way decision-making (3WD) method, combined with prospect theory and a non-additive measure, to cope with multi-source and incomplete risk information systems. Prospect theory improves the loss function of the original 3WD model, and the combination of non-additive measurement and probability measurement provides a new perspective to understand the meaning of decision-making, which could measure the relative degree by considering expert knowledge and objective data. The theoretical basis and framework of this model are illustrated, and this model is applied to a real in-service aviation equipment structures risk evaluation problem involving multiple incomplete risk information sources. When the simulation analysis is carried out, the results show that the availability of this method is verified. This method can also evaluate and rank key risk factors in equipment structures, which provides a reliable basis for decisions in aviation safety management.

摘要

由于属性的信息非独立性,再加上环境复杂多变,风险分析面临巨大困难。针对这一问题,本文提出一种新的三方决策(3WD)方法,结合前景理论和非可加测度,以应对多源且不完整的风险信息系统。前景理论改进了原3WD模型的损失函数,非可加测度与概率测度的结合为理解决策的意义提供了新视角,能够通过考虑专家知识和客观数据来衡量相对程度。阐述了该模型的理论基础和框架,并将此模型应用于一个涉及多个不完整风险信息源的实际在役航空装备结构风险评估问题。进行仿真分析时,结果表明该方法的有效性得到了验证。该方法还能对装备结构中的关键风险因素进行评估和排序,为航空安全管理决策提供可靠依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9069/11275305/18a1f0e74bd1/entropy-26-00598-g001.jpg

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