强化学习
作业车间调度
马尔可夫决策过程
计算机科学
调度(生产过程)
工作车间
动态优先级调度
地铁列车时刻表
工业工程
人工智能
流水车间调度
数学优化
马尔可夫过程
工程类
统计
操作系统
数学
作者
Hao Hu,Xiaofeng Jia,Qixuan He,Shifeng Fu,Kuo Li
标识
DOI:10.1016/j.cie.2020.106749
摘要
Driven by the recent advances in industry 4.0 and industrial artificial intelligence, Automated Guided Vehicles (AGVs) has been widely used in flexible shop floor for material handling. However, great challenges aroused by the high dynamics, complexity, and uncertainty of the shop floor environment still exists on AGVs real-time scheduling. To address these challenges, an adaptive deep reinforcement learning (DRL) based AGVs real-time scheduling approach with mixed rule is proposed to the flexible shop floor to minimize the makespan and delay ratio. Firstly, the problem of AGVs real-time scheduling is formulated as a Markov Decision Process (MDP) in which state representation, action representation, reward function, and optimal mixed rule policy, are described in detail. Then a novel deep q-network (DQN) method is further developed to achieve the optimal mixed rule policy with which the suitable dispatching rules and AGVs can be selected to execute the scheduling towards various states. Finally, the case study based on a real-world flexible shop floor is illustrated and the results validate the feasibility and effectiveness of the proposed approach.
科研通智能强力驱动
Strongly Powered by AbleSci AI