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.