Parallel Assembly Sequence Planning

This study introduces an advanced approach to the Parallel Assembly Sequence Planning (PASP) problem, addressing the complexities of task interdependencies and real-time adaptability across multiple parallel assembly lines. Traditional PASP methods often rely on static heuristics and struggle to accommodate dynamic production environments. To overcome these limitations, we propose the Advanced Priority Relationship Algorithm (APRA), a machine learning-enhanced framework that dynamically predicts assembly sequence parameters using a random forest model.

本研究针对并行装配序列规划（Parallel Assembly Sequence Planning, PASP）问题提出一种先进解决方案，旨在解决多条并行装配线场景下任务依赖关系复杂、实时适配性不足的难题。传统PASP方法多依赖静态启发式策略，难以适配动态变化的生产环境。为突破上述局限，本研究提出高级优先级关系算法（Advanced Priority Relationship Algorithm, APRA）——一种借助随机森林模型动态预测装配序列参数的机器学习增强框架。