
This paper focuses on the flexible packaging line for large length-to-diameter ratio heavy cylindrical products. It systematically analyzes the key bottleneck issues and proposes an optimized production line solution based on modular design. Based on the modular concept, the packaging process is decomposed into the loading module, inner packing processing module, packing module, sealing module, and stacking module. The functions of each module are clarified, and the connection sequence is optimized to achieve the compactness of the process flow and the efficient coordination of equipment. For the posture control, quality positioning, and safety protection requirements of single-root high-density materials during the packaging process, an “L-shaped + straight-line” layout scheme and dynamic scheduling strategy are proposed. A discrete-event model is constructed using the FlexSim simulation software. Through parameter calibration based on actual production data from 2022–2023, the effectiveness of the optimization scheme in improving equipment utilization and reducing buffer waiting time is verified, providing technical reference for the design of intelligent packaging lines for similar high-value and fragile materials. The proposed method and simulation framework are applicable to the optimization of production lines for imaging equipment and related materials.

In the printing and packaging industry, highly discrete production processes often result in serious material congestion in turnover areas. Inefficient workshop layouts hinder the smooth flow of materials, forming a key bottleneck to production efficiency. To address this, the authors propose a Systematic Layout Planning (SLP) method that integrates data-driven analysis with a genetic algorithm to optimize workshop layout for printing and packaging enterprises. The proposed method first builds a logistics intensity classification system based on traditional SLP principles, considering both logistics and non-logistics relationships between processing areas. Key parameters such as equipment capacity and material-handling frequency are incorporated to generate an initial layout. A multi-objective mathematical model is then established to minimize logistics costs and maximize non-logistics relations. A genetic algorithm is applied to iteratively optimize the layout using a fitness function that evaluates both logistics path lengths and relational proximity, with crossover and mutation strategies used to balance competing objectives. A case study of a representative printing and packaging enterprise demonstrates the effectiveness of the approach. The optimized layout reduces logistics costs by 28.15% and improves non-logistics coordination by 63%. Plant Simulation validates an 11.06% increase in production capacity and enhanced system flexibility. Compared with conventional experience-based layout methods, this approach integrates production data and evolutionary optimization, offering a data-driven solution to improve layout clarity and system adaptability in the printing and packaging sector.