Enhancing Shipyard Transportation Efficiency Through Dynamic Scheduling Using Digital Twin Technology

Uncertainties, such as road restrictions at shipyards and the irregular shape of blocks, pose challenges for transporter scheduling. Efficient scheduling of multiple transporters is critical to improving transportation efficiency. The digital twin (DT) technology offers numerous benefits, enabling interactions between the virtual and real worlds, real-time mapping, and dynamic performance evaluation. Based on DT technology, this study proposes a dynamic scheduling approach for cooperative transportation utilizing multiple transporters. The scheduling problem for multiple transporters is addressed and modeled in this study, considering factors such as block size and transporter loading. To solve this problem, a framework of DT-based multiple transporters system is established in a virtual environment. By inputting block information into this system, a solution is generated using transporter scheduling rules and interference detection methods. Experimental comparisons are conducted in this paper, exploring various scenarios with different number of tasks and the application of DT. The results demonstrate that the proposed approach effectively enhances transportation efficiency and improves ship construction efficiency. Hence, this study expands the application of DT technology in dynamic scheduling of transportation in shipyards and provides new ideas for shipbuilding company managers.

Disciplines

Administration and dimensions

Keywords

Shipyards block, DT, Genetic algorithms (GA), Cooperative transportation, Multiple transporters scheduling

Devices

To enhance the practicality of the research, the generated data is derived from the actual scheduling data of the Shanghai Hudong-Zhonghua Shipyard, in terms of task block information, incorporating relevant information such as task block details, transporters information, and other relevant factors.

Data

FileSizeFormatProcessingAccessend of embargo
Block data
1 KoCSVRaw data 2025-09-19
Distance dta
201 octetsCSVRaw data 2025-09-19
program
2 KoMATLABProcessed data 2025-09-19
How to cite
Miaomiao Sun, Chengji Liang, Daofang Chang, Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China, Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China, Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China (2024). Enhancing Shipyard Transportation Efficiency Through Dynamic Scheduling Using Digital Twin Technology. SEANOE. https://doi.org/10.17882/98388
In addition to properly cite this dataset, it would be appreciated that the following work(s) be cited too, when using this dataset in a publication :
Sun Miaomiao, Liang Chengji, Chang Daofang (2024). Enhancing shipyard transportation efficiency through dynamic scheduling using digital twin technology. PLOS ONE, 19 (2). https://doi.org/10.1371/journal.pone.0297069

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