Optimizing Batch Time Allocation in Tobacco Shred Production Through Smart Scheduling

This paper introduces a smart scheduling system aimed at optimizing batch time allocation in cigarette tobacco shred production. Traditional scheduling methods, which rely on fixed schedules based on historical data, often fail to adapt to dynamic production environments, leading to inefficiencies. To address these limitations, the system continuously monitors real-time data from the production line, including machine performance, material flow, and environmental conditions. Utilizing machine learning algorithms and predictive analytics, the system dynamically adjusts production schedules to anticipate needs, identify potential bottlenecks, and optimize resource allocation. The system was validated in a real production workshop, demonstrating significant improvements in flexibility, efficiency, and overall production quality. These advancements underscore the transformative potential of Industry 4.0 technologies in modern manufacturing.