A comprehensive risk assessment framework for occupational health and safety in pharmaceutical warehouses using Pythagorean fuzzy Bayesian networks

Risk assessment plays a crucial role in managing occupational health and safety in various industries, including pharmaceutical warehouses. Bayesian Networks (BN) have been widely employed for risk assessment due to their ability to handle uncertainty and quantify risks. However, the traditional BN approach has limitations in dealing with ambiguity and continuous variables. To address this, the fuzzy BN technique, combining fuzzy logic with BN, has emerged as an effective method for risk assessment. In this study, a fuzzy BN model using Pythagorean fuzzy sets is proposed for risk assessment in a pharmaceutical warehouse. The model incorporates 24 identified risk factors, and survey data is used to determine the conditional probabilities of these factors. The novelty of the study lies in the application of Pythagorean fuzzy sets and the development of risk assessment criteria specifically for pharmaceutical warehouses. The results of a comprehensive literature review and the proposed methodology are presented. A real case analysis is conducted, followed by validation and sensitivity analysis. The results provide valuable insights into enhancing occupational health and safety practices in pharmaceutical warehouses. The study contributes to enhancing occupational health and safety practices in pharmaceutical warehouses and provides a framework for future research.