A two stage risk assessment model based on interval-valued fuzzy numbers and risk attitudes

Due to the lack of historical data, experts often score risks based on their experience and some linguistic terms in risk assessment, then the risk assessment is essentially a semi-quantitative problem. When experts score risks, the scores are possibly related to experts' risk attitudes. On the other hand, the linguistic terms used are inevitably ambiguous, and interval-valued fuzzy numbers can deal with linguistic uncertainty better in complex situation. So a risk analysis model based on interval-valued fuzzy numbers and risk attitudes is novelly proposed in this paper. For safety risks in oil industry, the risk consequence often performs in several aspects, some of them are difficult to be measured by money and cannot be aggregated to a comprehensive index directly. A multi-expert and multi-criterion information fusion(MEMC-IF) model is needed. Firstly, linguistic terms and interval-valued fuzzy numbers are determined and a MEMC-IF model is constructed to derive the collective data and the comprehensive risk consequence. Secondly, a defuzzification model is presented to transform interval-valued fuzzy numbers to crisp values with considering risk attitudes novelly. Then, a risk matrix is constructed to assess which risks are serious and which risks can be ignored. In addition, a case study is demonstrated to show the efficiency of the proposed model and a discussion is completed.

Automated summary

In this paper, a risk analysis model based on interval-valued fuzzy numbers and risk attitudes is proposed to address the challenges in safety risks assessment in the oil industry, including linguistic uncertainty and multidimensional data fusion problems.