人-信息-物理互联环境下数字车间制造能力建模与自适应调度  

Digital Shop Floor Manufacturing Capability Modeling and Adaptive Scheduling in Human-cyber-physical Interconnected Environment

作　　者：胡炳涛 钟锐锐 冯毅雄[1,2] 杨晨 王天跃[1,4] 洪兆溪 谭建荣 HU Bingtao;ZHONG Ruirui;FENG Yixiong;YANG Chen;WANG Tianyue;HONG Zhaoxi;TAN Jianrong(State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou 310027;State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025;China Unicom(Zhejiang)Industrial Internet Co.,Ltd.,Hangzhou 311199;Department of Systems Engineering,City University of Hong Kong,Hong Kong 518057,China)

机构地区：[1]浙江大学流体动力与机电系统国家重点实验室,杭州310027 [2]贵州大学公共大数据国家重点实验室,贵阳550025 [3]联通(浙江)产业互联网有限公司,杭州311199 [4]香港城市大学系统工程系,中国香港518057

出　　处：《机械工程学报》2025年第3期23-39,共17页Journal of Mechanical Engineering

基　　金：中国博士后科学基金(2024T170795);国家自然科学基金(52205288);浙江省“尖兵”“领雁”研发攻关计划(2024C01029、2023C01214);浙江省教育厅一般科研(Y202352877);湖州市自然科学基金(2019YZ09)资助项目。

摘　　要：工业5.0的发展对制造业的信息化、数字化和智能化提出了更高的要求。针对传统车间制造能力组织范式和智能调度技术缺乏的重要难题,提出人-信息-物理互联环境下数字车间制造能力建模与自适应调度技术,从而实现对复杂车间制造能力的高保真建模与生产资源的高效调度。为了有效管理数字车间中的生产要素,提出一种融合人-信息-物理系统的数字车间制造能力建模技术。此外,提出一种深度强化学习驱动的数字车间自适应调度(Deep reinforcement learning-driven adaptive scheduling,DRL-AS)算法,该算法将柔性作业车间调度问题以异构析取图的形式进行建模。考虑到工序与机器之间复杂的耦合关联性,设计一种基于分层自注意力机制的多要素表征方法以提取环境状态的全局特征和辅助智能体进行高质量决策。近端策略优化(Proximal policy optimization,PPO)算法被用于训练所提出自适应调度技术。试验结果表明所提出方法的调度性能和泛化性能显著优于对比算法。The development of Industry 5.0 presents higher requirements for the informatization,digitization,and intelligence of the manufacturing industry.To address the important challenges of the lack of traditional workshop manufacturing capacity organizational paradigm and intelligent scheduling technology,a digital workshop manufacturing capacity modeling and adaptive scheduling technology in the human-cyber-physical interconnected environment is proposed to achieve high-fidelity modeling of complex workshop manufacturing capacity and efficient scheduling of production resources.In order to effectively manage the production elements in the digital workshop,a digital workshop manufacturing capacity modeling technology that integrates the human-cyber-physical system is proposed.In addition,a deep reinforcement learning-driven adaptive scheduling algorithm(DRL-AS)is devised for the digital workshop,which models the flexible job shop scheduling problem in the form of heterogeneous directed acyclic graphs.Considering the complex coupling relationship between operations and machines,a multi-factor representation method based on hierarchical self-attention mechanism is designed to extract global features of the environmental state and assist the agent in making high-quality decisions.Proximal policy optimization(PPO)algorithm is used to train the proposed adaptive scheduling technology.Experimental results show that the scheduling performance and generalization performance of the proposed method are significantly better than those of the comparison algorithms.

关 键 词：人-信息-物理系统 柔性作业车间调度 深度强化学习 制造能力建模 数字孪生 

分 类 号：TH165[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]