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Volume 9 Issue 11
Nov.  2022

IEEE/CAA Journal of Automatica Sinica

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L. J. Yue and  H. M. Fan,  “Dynamic scheduling and path planning of automated guided vehicles in automatic container terminal,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 2005–2019, Nov. 2022. doi: 10.1109/JAS.2022.105950
Citation: L. J. Yue and  H. M. Fan,  “Dynamic scheduling and path planning of automated guided vehicles in automatic container terminal,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 2005–2019, Nov. 2022. doi: 10.1109/JAS.2022.105950

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

doi: 10.1109/JAS.2022.105950
Funds:  This work was supported in part by the National Natural Science Foundation of China (61473053), the Science and Technology Innovation Foundation of Dalian, China (2020JJ26GX033)
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  • The uninterrupted operation of the quay crane (QC) ensures that the large container ship can depart port within laytime, which effectively reduces the handling cost for the container terminal and ship owners. The QC waiting caused by automated guided vehicles (AGVs) delay in the uncertain environment can be alleviated by dynamic scheduling optimization. A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems, in which the scheduling scheme determines the starting and ending nodes of paths, and the choice of paths between nodes affects the scheduling of subsequent AGVs. This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime. A dynamic optimization algorithm, including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm, is designed to solve the optimal AGV scheduling and path schemes. A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.

     

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    Highlights

    • AGV scheduling and path planning models are established to minimize costs
    • The uncertain factor of path conflicts and failed path nodes are considered
    • A rule-based heuristic algorithm composed of five principles is proposed
    • A hybrid algorithm that combines Dijkstra and Q-Learning algorithm is proposed
    • A novel multi-AGV conflict avoidance strategy based on graph theory is designed

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