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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 10
3038-3043

Research on AGV scheduling of shallow charging and shallow discharging charging strategy under reinforcement learning

Zhao Rui
Liang Chengji
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
DOI: 10.19734/j.issn.1001-3695.2024.03.0057

Abstract

For charging problem in AGV scheduling in automated container terminals, this paper constructed a mixed integer optimization model considering the shallow charging and shallow discharging charging strategy. The model aimed to minimize the final completion time of the AGV. Under the constraints of considering the change of AGV battery power and the difference in power consumption in different states of the AGV, the model used the AGV idle time and the end time of a work cycle to make up power, reducing the number of AGV charging times, and thus reducing the total completion time. The model was solved by Wolf-PHC reinforcement learning, and the results were compared with GAMS solver, Q-learning algorithm and genetic algorithm(GA) respectively to verify the effectiveness of the model and superiority of the algorithm. The example analysis shows that AGV utilization efficiency is higher under the shallow charging and shallow discharging charging strategy, and the combination of Wolf-PHC and GA is better for the model solution.

Foundation Support

国家自然科学基金资助项目(72271125)
上海市青年科技英才扬帆计划资助项目(21YF1416400)
上海市青年科技启明星计划资助项目(21QB1404800)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.03.0057
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 10
Section: Algorithm Research & Explore
Pages: 3038-3043
Serial Number: 1001-3695(2024)10-022-3038-06

Publish History

[2024-07-05] Accepted Paper
[2024-10-05] Printed Article

Cite This Article

赵锐, 梁承姬. 强化学习下浅充浅放充电策略AGV调度研究 [J]. 计算机应用研究, 2024, 41 (10): 3038-3043. (Zhao Rui, Liang Chengji. Research on AGV scheduling of shallow charging and shallow discharging charging strategy under reinforcement learning [J]. Application Research of Computers, 2024, 41 (10): 3038-3043. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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