Journal of System Simulation
Abstract
Abstract: A future tasks considering deep Q-network (F-DQN) algorithm was proposed to output realtime scheduling results of automated guided vehicles (AGVs) at automated terminals. This algorithm combined the advantages of real-time scheduling and static scheduling, improving the system status by considering static future task information when making real-time decisions, so as to obtain a better scheduling solution. In this study, the actual layout and equipment conditions of the Yangshan phase IV automated terminal were considered, and a series of simulation experiments were conducted using the Plant Simulation software. The experimental results show that the F-DQN algorithm can effectively solve the real-time scheduling problem of AGVs at automated terminals. Furthermore, the F-DQN algorithm significantly reduces the waiting time of quay cranes compared to the traditional DQN algorithm.
Recommended Citation
Liang, Chengji; Zhang, Shidong; Wang, Yu; and Lu, Bin
(2024)
"AGV Scheduling Problem at Automated Terminals Based on Improved DQN Algorithm,"
Journal of System Simulation: Vol. 36:
Iss.
11, Article 8.
DOI: 10.16182/j.issn1004731x.joss.23-0912
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss11/8
First Page
2592
Last Page
2603
CLC
TP391.9; TP181
Recommended Citation
Liang Chengji, Zhang Shidong, Wang Yu, et al. AGV Scheduling Problem at Automated Terminals Based on Improved DQN Algorithm[J]. Journal of System Simulation, 2024, 36(11): 2592-2603.
DOI
10.16182/j.issn1004731x.joss.23-0912
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