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基于模糊 Fine-Kinney 的 AHP-TOPSIS 方法在风险评估中的行动选择:以气田为例的研究

Action selection in risk assessment with fuzzy Fine-Kinney-based AHP-TOPSIS approach: a case study in gas plant.

机构信息

Industrial Engineering Department, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey.

出版信息

Environ Sci Pollut Res Int. 2022 Sep;29(44):66222-66234. doi: 10.1007/s11356-022-20498-2. Epub 2022 May 2.

Abstract

In this study, the hazards occurring in a medium-sized gas filling facility were defined, and the risk scores of these hazards were determined by the expert team according to the Fine-Kinney risk analysis method. However, since the same risk significance score is obtained in different combinations of scale values in the classical Fine-Kinney risk analysis method and the characteristics/constraints of the company applied in the risk analysis are not taken into account, the hazards were evaluated using fuzzy Fine-Kinney risk analysis, and the most critical hazards were determined. Action plans are defined for critical hazards determined as a result of fuzzy Fine-Kinney risk analysis. Among the actions that require company resources, the action selection was performed with the TOPSIS method, taking into account their relationship with the hazards by integrating the weights, which was calculated with the AHP method, of affected groups. The effect of operating constraints is included in the last step of the study to calculate the final weights. Calculating the results by including the risk-affected groups and company constraints and ranking the actions reveals that the study is an original, objective, and applicable study.

摘要

在本研究中,定义了中型气体充装设施中发生的危害,并由专家组根据 Fine-Kinney 风险分析方法确定这些危害的风险得分。然而,由于经典 Fine-Kinney 风险分析方法中不同规模值组合得到相同的风险意义得分,并且未考虑应用于风险分析的公司的特点/限制,因此使用模糊 Fine-Kinney 风险分析对危害进行了评估,并确定了最关键的危害。为模糊 Fine-Kinney 风险分析确定的关键危害制定了行动计划。在需要公司资源的行动中,考虑到与危害的关系,通过将与危害相关的影响群体的权重与 AHP 方法计算的权重进行整合,使用 TOPSIS 方法进行了行动选择。最后一步还包括操作限制的影响,以计算最终权重。通过包括风险影响群体和公司限制并对行动进行排名来计算结果表明,该研究是一项原创、客观和适用的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81a2/9059112/45b785bdb96d/11356_2022_20498_Fig1_HTML.jpg

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