用历史数据优化轮胎挤出工艺，减少65%准备时间
Data-Driven Differential Evolution in Tire Industry Extrusion: Leveraging Surrogate Models
- 基于历史数据构建代理模型，结合改进的差分进化算法
- 实现65%的初始化时间降低，显著减少材料浪费
- 适合无数学模型的工业过程优化，尤其制造领域
工业过程优化仍是重大挑战，尤其当目标函数或约束无明确数学表达时。本研究提出一种基于代理模型的数据驱动方法，仅利用历史过程数据优化复杂的现实制造系统。通过机器学习模型近似系统行为，构建代理模型，并集成到定制化的元启发式算法中：带多级惩罚函数与代理模型的数据驱动差分进化算法，该算法针对轮胎制造中的挤出过程特性进行了适配。目标是优化初始化参数以减少废料和生产时间。结果表明，基于代理模型的优化方法优于历史最优配置，在初始化和设置时间上减少65%，同时显著降低材料浪费。研究凸显了数据驱动建模与元启发式优化结合在缺乏显式数学表达的工业过程中的潜力。
原文摘要 · Abstract (English)
The optimization of industrial processes remains a critical challenge, particularly when no mathematical formulation of objective functions or constraints is available. This study addresses this issue by proposing a surrogate-based, data-driven methodology for optimizing complex real-world manufacturing systems using only historical process data. Machine learning models are employed to approximate system behavior and construct surrogate models, which are integrated into a tailored metaheuristic approach: Data-Driven Differential Evolution with Multi-Level Penalty Functions and Surrogate Models, an adapted version of Differential Evolution suited to the characteristics of the studied process. The methodology is applied to an extrusion process in the tire manufacturing industry, with the goal of optimizing initialization parameters to reduce waste and production time. Results show that the surrogate-based optimization approach outperforms historical best configurations, achieving a 65\% reduction in initialization and setup time, while also significantly minimizing material waste. These findings highlight the potential of combining data-driven modeling and metaheuristic optimization for industrial processes where explicit formulations are unavailable.
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