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

Author

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  • 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
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    References listed on IDEAS

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    1. 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).
    2. 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.
    3. 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.
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    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.
    2. Jiang, Yangsheng & Xia, Kui & Jiang, Haoran & Chen, Fei & Yao, Zhihong, 2024. "A spatiotemporal optimization method for connected and autonomous vehicle operations in long tunnel constructions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 651(C).

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