Intelligent Scheduling for Group Distributed Manufacturing Systems: Harnessing Deep Reinforcement Learning in Cloud-Edge Cooperation

Abstract

Cloud-edge technology enables near-real-time optimization of production lines in group-distributed manufacturing systems. Offloading some tasks to the cloud and processing the remaining tasks on the edge side can improve efficiency of the production optimization. However, due to the complexity of the manufacturing environment and various constraints, an effective offloading strategy is crucial to reduce computing delays and minimize transmission requirements for large-scale optimization requirements. This paper proposes a mixed-integer programming model and a deep reinforcement learning (DRL) framework, based on a Transformer, to address the cloud-edge offloading problem. The DRL framework consists of an encoder and decoder, designed using Transformer. Task offloading decisions are translated into two options: cloud offloading or edge retention. The encoder extracts relevant features for each option, and the decoder generates the probability of selecting each option based on the encoded information. Extensive computational experiments demonstrate the effectiveness of the proposed framework in solving the task offloading problem with time windows, achieving near-real-time optimization of production lines within competitive computational time.