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Digital-Twin-Driven AGV Scheduling and Routing in Automated Container Terminals

Author

Listed:
  • Ping Lou

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Yutong Zhong

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Jiwei Hu

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Chuannian Fan

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Xiao Chen

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Automated guided vehicle (AGV) scheduling and routing are critical factors affecting the operation efficiency and transportation cost of the automated container terminal (ACT). Searching for the optimal AGV scheduling and routing plan are effective and efficient ways to improve its efficiency and reduce its cost. However, uncertainties in the physical environment of ACT can make it challenging to determine the optimal scheduling and routing plan. This paper presents the digital-twin-driven AGV scheduling and routing framework, aiming to deal with uncertainties in ACT. By introducing the digital twin, uncertain factors can be detected and handled through the interaction and fusion of physical and virtual spaces. The improved artificial fish swarm algorithm Dijkstra (IAFSA-Dijkstra) is proposed for the optimal AGV scheduling and routing solution, which will be verified in the virtual space and further fed back to the real world to guide actual AGV transport. Then, a twin-data-driven conflict prediction method is proposed to predict potential conflicts by constantly comparing the differences between physical and virtual ACT. Further, a conflict resolution method based on the Yen algorithm is explored to resolve predicted conflicts and drive the evolution of the scheme. Case study examples show that the proposed method can effectively improve efficiency and reduce the cost of AGV scheduling and routing in ACT.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2678-:d:1169840
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    References listed on IDEAS

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    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. Cenk Sahin & Melek Demirtas & Rizvan Erol & Adil Baykasoğlu & Vahit Kaplanoğlu, 2017. "A multi-agent based approach to dynamic scheduling with flexible processing capabilities," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1827-1845, December.
    3. Xiaoyang Ma & Yongming Bian & Fei Gao, 2020. "An Improved Shuffled Frog Leaping Algorithm for Multiload AGV Dispatching in Automated Container Terminals," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, January.
    4. 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.
    5. Angeloudis, Panagiotis & Bell, Michael G.H., 2010. "An uncertainty-aware AGV assignment algorithm for automated container terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(3), pages 354-366, May.
    6. Luo, Jiabin & Wu, Yue, 2015. "Modelling of dual-cycle strategy for container storage and vehicle scheduling problems at automated container terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 49-64.
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    Cited by:

    1. Bong Gu Kang & Byeong Soo Kim, 2023. "A Study on Cognitive Error Validation for LED In-Ground Traffic Lights Using a Digital Twin and Virtual Environment," Mathematics, MDPI, vol. 11(17), pages 1-16, September.

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