Designing an Industrial Product Service System for Robot-Driven Sanding Processing Line: A Reinforcement Learning Based Approach

Abstract

The Industrial Product Service System (IPS2) is considered a sustainable and efficient business model, which has been gradually popularized in manufacturing fields since it can reduce costs and satisfy customization. However, a comprehensive design method for IPS2 is absent, particularly in terms of requirement perception, resource allocation, and service activity arrangement of specific industrial fields. Meanwhile, the planning and scheduling of multiple parallel service activities throughout the delivery of IPS2 are also in urgent need of resolution. This paper proposes a method containing service order design, service resource configuration, and service flow modeling to establish an IPS2 for robot-driven sanding processing lines. In addition, we adopt the modified Deep Q-network (DQN) to realize a scheduling scheme aimed at minimizing the total tardiness of multiple parallel service flows. Finally, our industrial case study validates the effectiveness of our methods for IPS2 design, demonstrating that the modified deep reinforcement learning algorithm reliably generates robust scheduling schemes.