Placement Path Optimization of Placement Machine Based on Rule Learning Iterative Method

This paper integrates deep reinforcement learning with meta-heuristic search rules to address the placement path optimization problem for placement machine. As a critical component of the surface mounting technology (SMT) production line, the production speed of the placement machine directly influences overall production efficiency. While the prevalent approach utilizes meta-heuristic algorithms for search, which enhances solution quality but consumes more time, this article introduces a reinforcement learning model to accelerate the search process. By organizing existing metaheuristic algorithm rules into an operator library, establishing and fine-tuning the corresponding policy network, and creating training and test datasets based on real-world conditions, the algorithm is developed and subjected to data training. Ultimately, a comparison with the machine's native algorithm and the classic genetic algorithm demonstrates the superiority of this approach.