IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2022i1p138-d1017079.html
   My bibliography  Save this article

A Three Stage Optimal Scheduling Algorithm for AGV Route Planning Considering Collision Avoidance under Speed Control Strategy

Author

Listed:
  • Chengji Liang

    (Institute of Logistics Science and Technology, Shanghai Maritime University, Shanghai 201306, China)

  • Yue Zhang

    (Institute of Logistics Science and Technology, Shanghai Maritime University, Shanghai 201306, China)

  • Liang Dong

    (Institute of Logistics Science and Technology, Shanghai Maritime University, Shanghai 201306, China)

Abstract

With the trend of terminal automation and the requirement for port operation efficiency to be greatly improved, it is very necessary to optimize the traveling route of automatic guided vehicles (AGV) with reference to the connection of loading and unloading equipment. As a complex multi-equipment system, it is inevitable that AGV will collide when traveling due to various accidents in actual operation, which will lead to AGV locking and reduce the efficiency of terminal operation. Considering the locking problem of AGV, we propose a three-stage integrated scheduling algorithm for AGV route planning. Through joint optimization with quay cranes (QC) and yard blocks, a road network model is established in the front area of the container port to optimize the path of AGV in the road network, and a speed control strategy is proposed to solve the problem of AGV collision avoidance. In the first stage, we establish the AGV optimal route model with the goal of minimizing the AGV path according to the AGV road network situation. In the second stage, on the basis of the determination of AGV route planning, and when the container task is known, the AGV task assignment model is established with the goal of minimizing the maximum completion time, and the model is solved by genetic algorithm (GA). In the third stage, on the basis of AGV task assignment and route determination, the AGV route and AGV task assignment scheme are input into the simulation model by establishing the AGV collision avoidance control model for speed control, and establishing the AGV route network simulation model for automated terminals considering collision avoidance in plant simulation software. The maximum completion time obtained from the simulation model is compared with the completion time obtained from the genetic algorithm. The proposed three-stage joint scheduling algorithm can improve the loading and unloading efficiency of the port, reduce the AGV locking situation, and has a certain contribution to the formulation of the actual operation planning of the port.

Suggested Citation

  • Chengji Liang & Yue Zhang & Liang Dong, 2022. "A Three Stage Optimal Scheduling Algorithm for AGV Route Planning Considering Collision Avoidance under Speed Control Strategy," Mathematics, MDPI, vol. 11(1), pages 1-18, December.
  • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:138-:d:1017079
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/1/138/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/1/138/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Errico, F. & Desaulniers, G. & Gendreau, M. & Rei, W. & Rousseau, L.-M., 2016. "A priori optimization with recourse for the vehicle routing problem with hard time windows and stochastic service times," European Journal of Operational Research, Elsevier, vol. 249(1), pages 55-66.
    2. Zhang, Yue & Liang, Chengji & Shi, Jian & Lim, Gino & Wu, Yiwei, 2022. "Optimal Port Microgrid Scheduling Incorporating Onshore Power Supply and Berth Allocation Under Uncertainty," Applied Energy, Elsevier, vol. 313(C).
    3. Kap Hwan Kim & Jong Wook Bae, 2004. "A Look-Ahead Dispatching Method for Automated Guided Vehicles in Automated Port Container Terminals," Transportation Science, INFORMS, vol. 38(2), pages 224-234, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    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. Fotuhi, Fateme & Huynh, Nathan & Vidal, Jose M. & Xie, Yuanchang, 2013. "Modeling yard crane operators as reinforcement learning agents," Research in Transportation Economics, Elsevier, vol. 42(1), pages 3-12.
    2. Zejun Tong & Chun Zhang & Xiaotai Wu & Pengcheng Gao & Shuang Wu & Haoyu Li, 2023. "Economic Optimization Control Method of Grid-Connected Microgrid Based on Improved Pinning Consensus," Energies, MDPI, vol. 16(3), pages 1-31, January.
    3. Bhoopalam, Anirudh Kishore & Agatz, Niels & Zuidwijk, Rob, 2018. "Planning of truck platoons: A literature review and directions for future research," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 212-228.
    4. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    5. Luciano Costa & Claudio Contardo & Guy Desaulniers, 2019. "Exact Branch-Price-and-Cut Algorithms for Vehicle Routing," Transportation Science, INFORMS, vol. 53(4), pages 946-985, July.
    6. Henry Lau & Ying Zhao, 2008. "Integrated scheduling of handling equipment at automated container terminals," Annals of Operations Research, Springer, vol. 159(1), pages 373-394, March.
    7. 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.
    8. Lijun Yue & Houming Fan & Chunxin Zhai, 2019. "Joint Configuration and Scheduling Optimization of a Dual-Trolley Quay Crane and Automatic Guided Vehicles with Consideration of Vessel Stability," Sustainability, MDPI, vol. 12(1), pages 1-16, December.
    9. Federica Bomboi & Christoph Buchheim & Jonas Pruente, 2022. "On the stochastic vehicle routing problem with time windows, correlated travel times, and time dependency," 4OR, Springer, vol. 20(2), pages 217-239, June.
    10. Kishore Bhoopalam, A. & van den Berg, R. & Agatz, N.A.H. & Chorus, C.G., 2021. "The long road to automated trucking: Insights from driver focus groups," ERIM Report Series Research in Management ERS-2021-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    11. Hossein Hashemi Doulabi & Gilles Pesant & Louis-Martin Rousseau, 2020. "Vehicle Routing Problems with Synchronized Visits and Stochastic Travel and Service Times: Applications in Healthcare," Transportation Science, INFORMS, vol. 54(4), pages 1053-1072, July.
    12. Jorge Oyola & Halvard Arntzen & David L. Woodruff, 2017. "The stochastic vehicle routing problem, a literature review, Part II: solution methods," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 349-388, December.
    13. Branislav Dragović & Ernestos Tzannatos & Nam Kuy Park, 2017. "Simulation modelling in ports and container terminals: literature overview and analysis by research field, application area and tool," Flexible Services and Manufacturing Journal, Springer, vol. 29(1), pages 4-34, March.
    14. Jonas F. Leon & Mohammad Peyman & Xabier A. Martin & Angel A. Juan, 2024. "Simulation of Heuristics for Automated Guided Vehicle Task Sequencing with Resource Sharing and Dynamic Queues," Mathematics, MDPI, vol. 12(2), pages 1-19, January.
    15. Wang, Yang & Bi, Mengyu & Lai, Jianhui & Wang, Chenxi & Chen, Yanyan & Holguín-Veras, José, 2024. "Recourse strategy for the routing problem of mobile parcel lockers with time windows under uncertain demands," European Journal of Operational Research, Elsevier, vol. 316(3), pages 942-957.
    16. Yang, Meng & Ni, Yaodong & Song, Qinyu, 2022. "Optimizing driver consistency in the vehicle routing problem under uncertain environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    17. Hoai Le & Adnan Yassine & Riadh Moussi, 2012. "DCA for solving the scheduling of lifting vehicle in an automated port container terminal," Computational Management Science, Springer, vol. 9(2), pages 273-286, May.
    18. Chen, Lu & Langevin, André & Lu, Zhiqiang, 2013. "Integrated scheduling of crane handling and truck transportation in a maritime container terminal," European Journal of Operational Research, Elsevier, vol. 225(1), pages 142-152.
    19. Zhaoxia Guo & Stein W. Wallace & Michal Kaut, 2019. "Vehicle Routing with Space- and Time-Correlated Stochastic Travel Times: Evaluating the Objective Function," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 654-670, October.
    20. Reusken, Meike & Laporte, Gilbert & Rohmer, Sonja U.K. & Cruijssen, Frans, 2024. "Vehicle routing with stochastic demand, service and waiting times — The case of food bank collection problems," European Journal of Operational Research, Elsevier, vol. 317(1), pages 111-127.

    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:2022:i:1:p:138-:d:1017079. 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.