Digital-twin based real-time resource allocation for hull parts picking and processing

Xiangdong Wang (), Xiaofeng Hu () and Jiafu Wan ()
Additional contact information
Xiangdong Wang: South China University of Technology
Xiaofeng Hu: Shanghai Jiao Tong University
Jiafu Wan: South China University of Technology

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 2, No 8, 613-632

Abstract: Abstract The development of Industrial Internet of Things, big data, and multi-domain modeling, led to the emergence of digital twin (DT), providing a new approach to the cyber-physical production systems. In the traditional shipbuilding industry, a large number of hull parts are often tracked and transferred. During the hull parts picking and processing, the uncertainty from parts transfer tasks and logistics information often leads to low resource utilization. Therefore, a real-time resource allocation method based on DT for hull part smart picking and processing system (SPPS) is proposed. Firstly, a multi-agent model of the multi-gantry crane system is established in virtual space to achieve real-time task allocation, hence minimizing the transport time. Next, a real-time picking and processing scheduling policy is proposed to reduce the idle time of all workstations by estimating the time of parts arriving at target station and processing completion time. Finally, the services available in the DT platform can be applied to optimize the system performance, taking the number of devices and the takt time optimization as examples. Several experiments in case study are carried out to verify the proposed method. The average utilization rate of all workstations is increased by 17.39%, and the standard deviation is reduced by 83.31%. The results have shown that the proposed method can effectively improve the workstation utilization rate and load balance. The maximum average number of parts in the station buffer, which is limited to 0.33, is kept at a low level.

Keywords: Resource allocation; Multi-gantry system; Digital twin; Real-time scheduling; Shipbuilding industry (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-022-02065-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:2:d:10.1007_s10845-022-02065-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-022-02065-1

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().