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