工业工程2025，Vol.28Issue(4)：15-23,9.DOI:10.3969/j.issn.1007-7375.240136
基于深度多智能体强化学习的机床混流装配线调度优化
Scheduling Optimization for Mixed-flow Assembly Lines of Machine Tools Based on Deep Multi-agent Reinforcement Learning
摘要
Abstract
In order to ensure the on-time delivery of machine tools in mixed-flow assembly shops,a scheduling optimization method based on improved deep multi-agent reinforcement learning is proposed,aiming to to address the low solution quality and slow training speed in minimizing production delays.A scheduling optimization model for mixed-flow assembly lines is constructed with the objective of minimizing delay time,where double deep Q network(DDQN)agents with decentralized execution are applied to learn the relationship between production information and scheduling objectives.The framework adopts the strategies of centralized training and decentralized execution,utilizing parameter sharing to deal with the non-stationary problem in multi-agent reinforcement learning.On this basis,a recurrent neural network is used to manage variable-length state and action representations,enabling agents to handle problems of arbitrary scale.A global/local reward function is also introduced to solve the reward sparsity problem in the training process.The optimal parameter combinations are identified through ablation experiments.Numerical experimental results show that,compared with the standard benchmarks,the proposed algorithm improves the average total delay of workpieces by 24.1%to 32.3%compared to before the improvement,and the training speed increased by 8.3%in terms of the achievement of the objective.关键词
机床混流装配线/深度多智能体强化学习/递归神经网络/全局/局部奖励函数Key words
machine tool mixed-flow assembly line/deep multi-agent reinforcement learning/recurrent neural network/global/local reward function分类
信息技术与安全科学引用本文复制引用
姜兴宇,陈嘉淇,王立权,徐伟宏..基于深度多智能体强化学习的机床混流装配线调度优化[J].工业工程,2025,28(4):15-23,9.基金项目
辽宁省"揭榜挂帅"科技计划资助项目(2022JH1/10800061) （2022JH1/10800061）
辽宁省教育厅高校基础科研重点攻关资助项目(LJKZZ20220023) （LJKZZ20220023）
沈阳市科学技术计划资助项目(23-503-6-01) （23-503-6-01）
