A perceptual computing–based method to prioritize intervention actions in the probabilistic risk assessment techniques

Probabilistic risk assessment techniques as the systematic tools have been widely used on the different type of industrial sectors to reduce the estimated risk to an acceptable level. The fact is to design inherently safety; hazards have to be eliminated and reduced in risk as much as possible with the consideration of several interventions. In this regard, multicriteria decision making (MCDM) science is commonly integrated with the probabilistic risk assessment techniques to improve the safety performance of a system. Thus, it has been widely used to assist decision makers in controlling the identified process hazards in a different type of engineering applications. However, by increasing the complexity of industrial sectors as well as human being judgments, typical MCDM methods cannot highly guarantee their output results. According to this point, proposing MCDM methods based on mathematical programming have been interested in scholars due to high reliability and feasibility of the results. In this paper, we extended integration of MULTIMOORA approach with the Choquet integral under subjectivity circumstances to prioritize corrective actions in a typical probabilistic risk assessment technique. To illustrate the efficiency and feasibility of the proposed method, it has been applied in a real case study.