App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

Optimization of AGV task assignment considering load balancing in rmfs

Tian Shuaihui
Shen Yifan
Ou Liying
Fan Lue
School of Modern Postal College, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0576

Abstract

To enhance the efficiency of RMFS and reduce operating costs, we propose a double-layer planning model for AGV task allocation considering load balancing, following an analysis of key factors causing congestion in RMFS. The upper layer seeks to minimize the total cost, while the lower layer addresses the minimization of the system load standard deviation and AGV idle rate through a multi-objective function. We introduce a modified adaptive genetic algorithm (SAGA) to overcome the challenges of low efficiency and susceptibility to local optima in solving task allocation problems using traditional GA. The Sigmoid function is integrated to transform fitness values and participates in the adaptive adjustment of cross-mutation operators. Additionally, catastrophic strategies are incorporated to prevent premature convergence of the algorithm. Finally, we simulated the proposed algorithm on different scale cases, and the results demonstrate that, in comparison to the genetic algorithm, adaptive genetic algorithm, adaptive disaster genetic algorithm, and ant colony algorithm, the improved algorithm exhibits substantial optimization in the experimental results of various scale cases. This suggests that the enhanced algorithm effectively avoids premature convergence, enhances solution quality and convergence stability, actively balances road network load, and reduces total operating costs.

Foundation Support

重庆市教育委员会人文社会科学研究项目(22SKGH127)
中国物流学会、中国物流与采购联合会研究课题(2023CSLKT3-385)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0576
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-02-05] Accepted Paper

Cite This Article

田帅辉, 沈亦凡, 欧丽英, 等. 考虑负载量均衡的自动拣货系统AGV任务分配优化 [J]. 计算机应用研究, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0576. (Tian Shuaihui, Shen Yifan, Ou Liying, et al. Optimization of AGV task assignment considering load balancing in rmfs [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0576. )

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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