R-DMDQN: A rule embedding based dynamic multi-objective deep Q-network for mass-individualized production scheduling of printed circuit board

Abstract

The printed circuit board (PCB) industry is currently facing the challenge of mass customization demands, which places an urgent need for efficient scheduling in PCB production. Due to the production process’s complexity and the environment’s variability, traditional scheduling algorithms often fail to achieve optimal performance in practical applications. This paper establishes a dynamic multi-objective flexible PCB shop scheduling model to address the challenges above. The model uses total tardiness, maximum completion time, and average machine utilization as optimization objectives. Moreover, a rule-embedded deep Q-network (R-DMDQN) algorithm is developed to address the complex dynamic characteristics of the PCB production process. The algorithm integrates characteristics of PCB production, extracting seven selected features to describe the system state. Simultaneously, it embeds six composite scheduling rules developed and guided by specialized knowledge to enhance the interpretability of learned strategies, and to augment the adaptability and flexibility of the algorithm. Through extensive experimental verification, the results show that the R-DMDQN model proposed in this study has significant superiority and stability in improving scheduling performance compared to the existing well-known scheduling rules and the NSGA-II algorithm. The research provides an innovative approach to the automation and optimization of scheduling in the PCB industry. It is expected to promote the application of related technologies in other complex production systems.