南京大学学报（自然科学版）2025，Vol.61Issue(4)：583-598,16.DOI:10.13232/j.cnki.jnju.2025.04.005
基于深度强化学习的智能制造多智能体协作的通信与任务调度研究
Deep reinforcement learning-based multi-agent cooperative communication and task scheduling for smart manufacturing
摘要
Abstract
This study addresses the limitations of cross-group communication constraints,personalized goal absence,and unconscious interactions in task scheduling-oriented multi-agent structural collaborative communication within intelligent manufacturing.We propose a Goal-Oriented(GO)learnable multi-agent structural dynamic collaboration model(GOLSC)integrating Deep Q Network(DQN)with Dijkstra's algorithm,which enhances Learning Structural Communication(LSC)by incorporating autonomous communication awareness.The framework establishes a dynamic task scheduling model by pairing each machine with an agent,while a dedicated manager agent tracks workpiece states and monitors dynamic events.By implementing GOLSC-enhanced static allocation rules and dynamic job scheduling strategies for the machine agents to select unfinished workpieces,the model achieves coordinated optimization of collaboration efficiency and production responsiveness.As the scale of the agents continues to grow,the tardiness rate of our model decreases by 20%～70%compared to traditional communication-free models and 10%～40%compared to structural communication models,and the average bandwidth occupancy rate is reduced by 10%～15%,effectively addressing the production inefficiencies caused by the lack of adaptability to dynamic events and interaction among agents in conventional intelligent manufacturing workshops.关键词
智能制造/深度强化学习/多智能体协作/结构化通信/动态作业车间调度Key words
smart manufacturing/deep reinforcement learning/multi-agent collaboration/structural communication/dynamic job-shop scheduling分类
机械制造引用本文复制引用
樊子靖,郭银章..基于深度强化学习的智能制造多智能体协作的通信与任务调度研究[J].南京大学学报（自然科学版）,2025,61(4):583-598,16.基金项目
中央引导地方科技发展资金(YDZJSX20231A044),智能信息处理山西省重点实验室开放课题(CICIP2023001) （YDZJSX20231A044）
