Optimizing Just-in-Time Scheduling: Comparative Analysis Using

Abstract

The advent of Industry 4.0 and the emerging concepts of Industry 5.0 have revolutionized manufacturing processes, emphasizing the integration of smart technologies and human-centric approaches. Industry 4.0 focuses on the automation and interconnectivity of manufacturing systems, while Industry 5.0 highlights the collaboration between humans and machines to enhance productivity and innovation. Despite the advancements brought by these industrial revolutions, a significant gap remains in the optimization of Just-In-Time (JIT) scheduling models, particularly when incorporating human factors. Traditional JIT scheduling primarily centers around machine efficiency, often neglecting the implications of human operator fatigue and the necessity for breaks. This oversight can lead to decreased productivity, increased risks of work accidents and health issues job completion times, and higher tardiness penalties. This project addresses this gap by proposing a comprehensive JIT precast production scheduling model that integrates human factors. Three distinct models were developed and analyzed: the first model focuses on JIT scheduling for fully automated machines, optimizing job completion without considering human involvement; the second model incorporates human operators without scheduling breaks, examining the impact of human factors such as fatigue and availability on job completion times and tardiness; the third model introduces scheduled breaks for human operators, aiming to mitigate fatigue and improve overall efficiency. By comparing these models, we evaluate the effects of incorporating human factors and breaks on key performance indicators (KPIs) such as job completion times, earliness, tardiness, and operational costs. The findings demonstrate the importance of human-centric scheduling in achieving sustainable and efficient production processes, underscoring the potential benefits of integrating breaks to manage operator fatigue.