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The tobacco industry has long been a cornerstone of global commerce, evolving into a highly specialized sector where precise and consistent production processes are vital. Among these processes, tobacco shred production plays a pivotal role in determining product quality. This stage involves the careful cutting, drying, and conditioning of tobacco leaves to produce uniform shreds, ensuring consistency in size, moisture content, and texture. The uniformity of these shreds is critical because any deviations can lead to significant variations in the final product, consequently impacting consumer satisfaction and brand loyalty. Recent studies have emphasized the role of advanced machine learning models and segmentation algorithms in maintaining product quality throughout this process (Liu et al., 2024; Xie et al., 2024).
As global competition intensifies, tobacco manufacturers face increasing pressure to optimize production processes. Liu et al. (2023) proposed that the optimization efforts in this domain should focus on both maintaining high product quality and enhancing operational efficiency to reduce costs and increase profitability. However, these optimizations are often challenged by the numerous variables inherent in the production process, including raw material conditions, machine performance, and environmental factors, such as temperature and humidity. Effective management of these factors is essential, especially as real-time monitoring and adaptive control systems have become integral in modern production environments (He et al., 2023; Lu et al., 2023).
Traditional scheduling methods, such as static scheduling and heuristic approaches, have long been relied upon in production management. These methods typically involve fixed schedules based on historical data and average production conditions. They provide a baseline for operations, but they often lack the flexibility needed in dynamic environments. Lin et al. (2023) highlighted the inadequacy of these methods in situations where unforeseen events, such as equipment malfunctions or sudden changes in material properties, can disrupt production, leading to delays and inefficiencies.
Adaptive scheduling methods have been proposed to address these limitations. However, many existing adaptive methods still rely on heuristic adjustments that may not fully capture the complexity of modern production systems. Static scheduling approaches, which rely heavily on historical averages, lack the flexibility to adapt to real-time fluctuations; hence, they lead to bottlenecks and suboptimal resource utilization (Liang et al., 2023; Xue et al., 2023). As Chen et al. (2023) argued, these inefficiencies become more pronounced as production environments become increasingly complex.
The growing complexity of production systems has led researchers to explore more adaptive and intelligent scheduling solutions. Chang et al. (2023) proposed integrating machine learning-driven adaptive scheduling mechanisms, supported by IoT systems, to improve flexibility and resilience in manufacturing operations. However, there remains a gap in the application of advanced optimization algorithms, such as swarm intelligence and metaheuristic methods, in real-time scheduling systems. Similarly, Feng et al. (2022) emphasized the importance of Industry 4.0 technologies in enabling real-time data-driven decisions, which are crucial for optimizing production schedules. These advancements are particularly relevant in tobacco production, where maintaining consistent quality amid varying conditions is critical.
In response to these challenges, a surge in the development of smart scheduling systems has emerged. Zahid et al. (2024) proposed leveraging Industry 4.0 principles to create systems that dynamically adjust production schedules based on real-time data, addressing the limitations of static methods. Such systems continuously monitor key production variables—such as machine performance, material flow rates, and environmental conditions—and apply predictive analytics to optimize scheduling processes (Liang et al., 2024; Chouikhi et al., 2023). The ability to anticipate disruptions before they occur allows for proactive adjustments, ensuring consistent production quality and efficiency (Wang et al., 2024).