<![CDATA[The metal casting industry faces significant challenges in balancing productivity with worker safety and well-being. Hazardous working conditions, including high temperatures, exposure to gases, and repetitive motions, increase the risk of injuries and fatigue. 1 This study proposes a novel hybrid approach that integrates Genetic Algorithm (GA) and Fuzzy Logic (FL) to optimize workstation ergonomics. The system utilizes real-time data from sensors to evaluate ergonomic factors such as worker posture, fatigue levels, and environmental conditions. Fuzzy Logic processes this data, while GA optimizes the system's parameters for enhanced accuracy and adaptability. Experimental results demonstrated significant improvements, including a 25% reduction in worker fatigue, a 30% improvement in air quality compliance, and a 35% decrease in ergonomic risks. Real-time adjustments, such as desk height modifications and improved ventilation, effectively enhanced worker safety and comfort. This innovative approach offers a scalable and reliable solution for improving ergonomics in dynamic industrial environments, contributing to both worker well-being and operational efficiency. Future research could further enhance the system by incorporating machine learning for improved predictive capabilities and expanded optimization of ergonomic parameters.]]>
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