Deep Learning for Intelligent Production Scheduling Optimization

In the era of Industry 4.0 and intelligent manufacturing, optimizing production scheduling is crucial for enhancing efficiency and economic returns, amidst complex challenges.Traditional scheduling methods often struggle with the demands of intelligent production, particularly in managing complex systems and uncertainties.This study aims to refine production scheduling in intelligent manufacturing using advanced deep learning techniques, proposing an optimized simulation model that considers key factors such as workshop failure rates, workpiece path selection, layout, and utilization rates.Additionally, it introduces a cutting-edge scheduling approach based on multi-agent deep reinforcement learning, incorporating an attention mechanism in an advantage actor-critic framework, complemented by a global reward function to improve production outcomes.This research not only offers a new avenue for optimizing intelligent production scheduling but also provides a valuable simulation tool, contributing significantly to the intelligent transformation of manufacturing.