Distributed heterogeneous flexible job-shop scheduling problem considering automated guided vehicle transportation via improved deep Q network

被引:2
作者
Yuan, Minghai [1 ]
Lu, Songwei [1 ]
Zheng, Liang [1 ]
Yu, Qi [1 ]
Pei, Fengque [1 ]
Gu, Wenbin [1 ]
机构
[1] Hohai Univ, Coll Mech & Elect Engn, Changzhou, Peoples R China
关键词
Distributed heterogeneous flexible job-shop; scheduling; AGV; Deep reinforcement learning; Deep Q network; Combination dispatching rule; OPTIMIZATION;
D O I
10.1016/j.swevo.2025.101902
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Distributed manufacturing has become a research hotspot in the context of economic globalization. The distributed heterogeneous flexible job-shop scheduling problem considering automated guided vehicle transportation (DHFJSP-AGV) extends the classic flexible job-shop scheduling problem (FJSP) but remains under- explored. DHFJSP-AGV involves four subproblems: assigning jobs to heterogeneous factories, scheduling jobs to machines, sequencing operations on machines and transporting jobs between machines using AGVs. Due to its complexity, this study proposes an improved deep Q network (DQN) real-time scheduling method aimed at minimizing makespan. A mixed integer linear programming model (MILP) of DHFJSP-AGV is developed and transformed into a Markov decision process (MDP). Eight general state features are extracted and normalized to represent the state space, while appropriate combination dispatching rules are selected as the action space. The state features of each scheduling point are input to the DQN, determining the factory, job, machine, and AGV for each process. Additionally, double DQN and an improved epsilon-greedy exploration are used to enhance the DQN. Numerical comparison experiments under different production configurations and real-world application in distributed flexible job-shop with dynamic map environment demonstrate the effectiveness and generalization capabilities of improved DQN.
引用
收藏
页数:18
相关论文
共 50 条
[41]   Dynamic scheduling for flexible job-shop with reconfigurable manufacturing cells considering dynamic job arrivals based on deep reinforcement learning [J].
Zheng, Liang ;
Chen, Xiaodi ;
Zhuang, Cunbo ;
Liu, Jianhua ;
Zhang, Yongyang ;
Lai, Lijuan .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2025,
[42]   Multi-objective flexible job-shop scheduling via graph attention network and reinforcement learning [J].
Li, Yuanhe ;
Zhong, Wenjian ;
Wu, Yuanqing .
JOURNAL OF SUPERCOMPUTING, 2025, 81 (01)
[43]   Research of Flexible Assembly Job-Shop Batch-Scheduling Problem Based on Improved Artificial Bee Colony [J].
Li, Xiulin ;
Lu, Jiansha ;
Yang, Chenxi ;
Wang, Jiale .
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2022, 10
[44]   A Hierarchical Multi-Action Deep Reinforcement Learning Method for Dynamic Distributed Job-Shop Scheduling Problem With Job Arrivals [J].
Huang, Jiang-Ping ;
Gao, Liang ;
Li, Xin-Yu .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2025, 22 :2501-2513
[45]   A Hierarchical Multi-Action Deep Reinforcement Learning Method for Dynamic Distributed Job-Shop Scheduling Problem With Job Arrivals [J].
Huang, Jiang-Ping ;
Gao, Liang ;
Li, Xin-Yu .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2025, 22 :2501-2513
[46]   A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time [J].
Huang, Xiabao ;
Yang, Lixi .
INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING AND CYBERNETICS, 2019, 12 (02) :154-174
[47]   Solving the blocking job-shop scheduling problem considering transportation and the no buffer constraint: a performance and price-based auction approach [J].
Chen, Shiming ;
Zeng, Chengkuan ;
Tang, Jiafu ;
Yan, Chongjun .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2025,
[48]   A Q-learning improved differential evolution algorithm for human-centric dynamic distributed flexible job shop scheduling problem [J].
Li, Xixing ;
Guo, Ao ;
Yin, Xiyan ;
Tang, Hongtao ;
Wu, Rui ;
Zhao, Qingqing ;
Li, Yibing ;
Wang, Xivincent .
JOURNAL OF MANUFACTURING SYSTEMS, 2025, 80 :794-823
[49]   Flexible job shop scheduling via deep reinforcement learning with meta-path-based heterogeneous graph neural network [J].
Wan, Lanjun ;
Fu, Long ;
Li, Changyun ;
Li, Keqin .
KNOWLEDGE-BASED SYSTEMS, 2024, 296
[50]   Evolutionary algorithm incorporating reinforcement learning for energy-conscious flexible job-shop scheduling problem with transportation and setup times [J].
Zhang, Guohui ;
Yan, Shaofeng ;
Song, Xiaohui ;
Zhang, Deyu ;
Guo, Shenghui .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 133