Prioritization of Human Factors Variables in the Management of Major Accident Hazards in Process Industries Using Fuzzy AHP Approach

Abstract

Background: Human factors play an important role in process safety and can have dramatic effects on the safety performance of organizations. In addition, human factors are very important elements in safety management system and management of major accident hazards in process industries. Many variables of human factors may affect the safety in process industries, which need to be addressed in a more holistic approach. Objectives: This study was designed to determine the most important variables of human factors in the management of major accident hazards in process industries through the fuzzy analytic hierarchy process (fuzzy AHP) approach in 2017. Methods: After specifying human factors variables including the job, the individual (personal), and the organization factors and related sub-factors, fuzzy AHP approach was proposed to determine the weights and the degree of importance of factors and related sub-factors. Factors and related sub-factors were weighted using triangular fuzzy numbers in pairwise comparisons and fuzzy linguistic variables. Results: Organization factors got the highest relative weights (0.381) in comparison with other studied factors. Among organization sub-factors, safety culture had the highest importance (0.33). Among individual sub-factors, competence of personnel is recognized as the most important sub-factor (0.37). The highest relative weights for job sub-factors were obtained for ergonomic design and environmental factors (0.17). Conclusions: Determining and identifying the most important variables of human factors in process safety are very important for organizations. The present study demonstrates that organization factors are the most important variables in the management of major accident hazards in process industries with the application of fuzzy AHP. Safety culture, staff competence, ergonomic design, and environmental factors are the most important sub-factors.