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Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop

Author

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  • Nan Zhao

    (School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China)

  • Chun Feng

    (School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
    National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756, China)

Abstract

In the context of the continuous development and maturity of intelligent manufacturing and intelligent logistics, it has been observed that the majority of vehicle maintenance in EMU trains still relies on traditional methods, which are characterized by excessive manual intervention and low efficiency. To address these deficiencies, the present study proposes the integration of Automatic Guided Vehicles (AGVs) to improve the traditional maintenance processes, thereby enhancing the efficiency and quality of vehicle maintenance. Specifically, this research focuses on the scenario of the maintenance workshop in EMU trains and investigates the task allocation problem for multiple AGVs. Taking into consideration factors such as the maximum load capacity of AGVs, remaining battery power, and task execution time, a mathematical model is formulated with the objective of minimizing the total distance and time required to complete all tasks. A multi-population genetic algorithm is designed to solve the model. The effectiveness of the proposed model and algorithm is validated through simulation experiments, considering both small-scale and large-scale scenarios. The results indicate that the multi-population genetic algorithm outperforms the particle swarm algorithm and the genetic algorithm in terms of stability, optimization performance, and convergence. This research provides scientific guidance and practical insights for enterprises adopting task allocation strategies using multiple AGVs.

Suggested Citation

  • Nan Zhao & Chun Feng, 2023. "Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3509-:d:1217000
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    References listed on IDEAS

    as
    1. Yu Zhou & Leishan Zhou & Yun Wang & Zhuo Yang & Jiawei Wu, 2017. "Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem," Complexity, Hindawi, vol. 2017, pages 1-14, July.
    2. Rajnish Kler & Roshan Gangurde & Samariddin Elmirzaev & Md Shamim Hossain & Nhut V. T. Vo & Tien V. T. Nguyen & P. Naveen Kumar & Peiman Ghasemi, 2022. "Optimization of Meat and Poultry Farm Inventory Stock Using Data Analytics for Green Supply Chain Network," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-8, October.
    3. Min Chen & Ashutosh Sharma & Jyoti Bhola & Tien V. T. Nguyen & Chinh V. Truong, 2022. "Multi-agent task planning and resource apportionment in a smart grid," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 444-455, March.
    4. Maryam Mousavi & Hwa Jen Yap & Siti Nurmaya Musa & Farzad Tahriri & Siti Zawiah Md Dawal, 2017. "Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-24, March.
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