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Grid-Map-Based Path Planning and Task Assignment for Multi-Type AGVs in a Distribution Warehouse

Author

Listed:
  • Zhuoling Jiang

    (Department of Logistics Engineering, School of Traffic and Transportation, Beijing Jiaotong University, Shangyuan Cun, Haidian District, Beijing 100044, China)

  • Xiaodong Zhang

    (Department of Logistics Engineering, School of Traffic and Transportation, Beijing Jiaotong University, Shangyuan Cun, Haidian District, Beijing 100044, China)

  • Pei Wang

    (Department of Logistics Engineering, School of Traffic and Transportation, Beijing Jiaotong University, Shangyuan Cun, Haidian District, Beijing 100044, China)

Abstract

In an intelligent distribution warehouse, latent AGVs are used for horizontal handling, and forklift AGVs are used for horizontal or vertical handling. Studying the path planning and task assignment problem when the two types of AGVs are mixed can help improve the warehouse operation efficiency and reduce the warehouse operation cost. This paper proposes a two-stage optimization method to solve this problem. In the first stage, the warehouse plan layout is transformed into a raster map, and the shortest path between any two points of the warehouse without conflict with fixed obstacles is planned and stored using the A* algorithm combined with circular rules, and the planned shortest path is called directly in the subsequent stages. In the second stage, to minimize the task completion time and AGV energy consumption, a genetic algorithm combining penalty functions is used to assign horizontal handling tasks to submerged AGVs or forklift AGVs and vertical handling tasks to forklift AGVs. The experimental results show that the method can meet the 24 h operation requirements of an intelligent distribution warehouse and realize the path planning and task assignment of forklift AGVs and latent AGVs. And furthermore, the number of AGVs arranged in the warehouse can be further reduced.

Suggested Citation

  • Zhuoling Jiang & Xiaodong Zhang & Pei Wang, 2023. "Grid-Map-Based Path Planning and Task Assignment for Multi-Type AGVs in a Distribution Warehouse," Mathematics, MDPI, vol. 11(13), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2802-:d:1176495
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    References listed on IDEAS

    as
    1. Aziez, Imadeddine & Côté, Jean-François & Coelho, Leandro C., 2022. "Fleet sizing and routing of healthcare automated guided vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    2. Wenxiang Xu & Shunsheng Guo & Xixing Li & Chen Guo & Rui Wu & Zhao Peng, 2019. "A Dynamic Scheduling Method for Logistics Tasks Oriented to Intelligent Manufacturing Workshop," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-18, April.
    3. 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.
    4. Nirup N. Krishnamurthy & Rajan Batta & Mark H. Karwan, 1993. "Developing Conflict-Free Routes for Automated Guided Vehicles," Operations Research, INFORMS, vol. 41(6), pages 1077-1090, December.
    5. Rafal Szczepanski & Artur Bereit & Tomasz Tarczewski, 2021. "Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality," Energies, MDPI, vol. 14(20), pages 1-14, October.
    6. Jianxun Li & Wenjie Cheng & Kin Keung Lai & Bhagwat Ram, 2022. "Multi-AGV Flexible Manufacturing Cell Scheduling Considering Charging," Mathematics, MDPI, vol. 10(19), pages 1-15, September.
    7. Kaibo Liang & Li Zhou & Jianglong Yang & Huwei Liu & Yakun Li & Fengmei Jing & Man Shan & Jin Yang, 2023. "Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System," Mathematics, MDPI, vol. 11(7), pages 1-29, March.
    Full references (including those not matched with items on IDEAS)

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