IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i13p2802-d1176495.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/13/2802/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/13/2802/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    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. 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.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ballis, Athanasios & Golias, John, 2004. "Towards the improvement of a combined transport chain performance," European Journal of Operational Research, Elsevier, vol. 152(2), pages 420-436, January.
    2. Chiang, Wen-Chyuan & Kouvelis, Panagiotis & Urban, Timothy L., 2006. "Single- and multi-objective facility layout with workflow interference considerations," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1414-1426, November.
    3. Hernan Caceres & Rajan Batta & Qing He, 2017. "School Bus Routing with Stochastic Demand and Duration Constraints," Transportation Science, INFORMS, vol. 51(4), pages 1349-1364, November.
    4. Vis, Iris F.A., 2006. "Survey of research in the design and control of automated guided vehicle systems," European Journal of Operational Research, Elsevier, vol. 170(3), pages 677-709, May.
    5. Jenny Nossack & Dirk Briskorn & Erwin Pesch, 2018. "Container Dispatching and Conflict-Free Yard Crane Routing in an Automated Container Terminal," Transportation Science, INFORMS, vol. 52(5), pages 1059-1076, October.
    6. R Gopalan & N S Narayanaswamy, 2009. "Analysis of algorithms for an online version of the convoy movement problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1230-1236, September.
    7. Adrian Kampa & Iwona Paprocka, 2021. "Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times," Energies, MDPI, vol. 14(21), pages 1-25, November.
    8. Ping Lou & Yutong Zhong & Jiwei Hu & Chuannian Fan & Xiao Chen, 2023. "Digital-Twin-Driven AGV Scheduling and Routing in Automated Container Terminals," Mathematics, MDPI, vol. 11(12), pages 1-25, June.
    9. Min Zhang & Rajan Batta & Rakesh Nagi, 2009. "Modeling of Workflow Congestion and Optimization of Flow Routing in a Manufacturing/Warehouse Facility," Management Science, INFORMS, vol. 55(2), pages 267-280, February.
    10. Yuan Gao & Qian Zhang & Chun Kit Lau & Bhagwat Ram, 2022. "Robust Appointment Scheduling in Healthcare," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    11. Ballis, Athanasios & Golias, John, 2002. "Comparative evaluation of existing and innovative rail-road freight transport terminals," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(7), pages 593-611, August.
    12. Moussa Abderrahim & Abdelghani Bekrar & Damien Trentesaux & Nassima Aissani & Karim Bouamrane, 2020. "Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints," Energies, MDPI, vol. 13(18), pages 1-19, September.
    13. Wen-Chyuan Chiang & Panagiotis Kouvelis & Timothy L. Urban, 2002. "Incorporating Workflow Interference in Facility Layout Design: The Quartic Assignment Problem," Management Science, INFORMS, vol. 48(4), pages 584-590, April.
    14. Yangkun Xia & Zhuo Fu & Lijun Pan & Fenghua Duan, 2018. "Tabu search algorithm for the distance-constrained vehicle routing problem with split deliveries by order," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-19, May.
    15. Gamache, Michel & Grimard, Renaud & Cohen, Paul, 2005. "A shortest-path algorithm for solving the fleet management problem in underground mines," European Journal of Operational Research, Elsevier, vol. 166(2), pages 497-506, October.
    16. Kaspar Schüpbach & Rico Zenklusen, 2013. "An adaptive routing approach for personal rapid transit," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 77(3), pages 371-380, June.
    17. Wenxiang Xu & Shunsheng Guo, 2019. "A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
    18. Lulu Cheng & Ning Zhao & Kan Wu, 2024. "Stochastic Multi-Objective Multi-Trip AMR Routing Problem with Time Windows," Mathematics, MDPI, vol. 12(15), pages 1-22, July.
    19. 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.
    20. Dipesh J. Patel & Rajan Batta & Rakesh Nagi, 2005. "Clustering Sensors in Wireless Ad Hoc Networks Operating in a Threat Environment," Operations Research, INFORMS, vol. 53(3), pages 432-442, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2802-:d:1176495. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.