A fuzzy logic approach in the definition of risk acceptance boundaries in occupational safety and health

Abstract

Organizations need to make decisions about risk acceptance, to decide about the need of risk-reducing measures. In this process, the personal judgments of occupational safety and health (OSH) practitioners have great importance. If on one hand, they have the technical knowledge about risk; on the other hand, the decisions can be dependent on their level of risk acceptance. This paper analyzes judgments of OSH practitioners about the level of risk acceptance, using the fuzzy logic approach. A questionnaire to analyze the reported level of risk acceptance was applied. The questionnaire included 79 risk scenarios, each accounting for the frequency of an accident with more lost workdays than a given magnitude. Through the two-step cluster analysis, three groups of OSH practitioners were identified: unacceptable, tolerable, and realistic groups. A further analysis of the realistic group judgments about risk was performed, using the fuzzy logic approach. The fuzzy sets of input and output variables were determined, and the relationship between the variables was mapped through fuzzy rules. After that, the min–max fuzzy inference method was used. The obtained results show that the risk level is acceptable when input variables are at the lowest value and unacceptable when the risk level is high. The obtained results allow us to better understand the modeling of OSH practitioners’ judgments about risk acceptance, noting the uncertainty related to these judgments.