期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 60, 期 3, 页码 1016-1035出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2020.1849846
关键词
Multi-fidelity simulation-based optimisation; digital twin shop floor; large-scale problem optimisation; simulation; heuristic algorithms
资金
- China Postdoctoral Science Foundation [2019M652665]
- National Natural Science Foundation of China [51561125002, 71620107002, 51705379]
- National Key Research and Development Program of China [2018YFB1702700]
- Youth Program of National Natural Science Foundation of China [51905196]
- Business Intelligence Center
- EMLYONShanghai Campus
Digital twin technology has gained considerable attention in the implementation of Industry 4.0 and intelligent manufacturing. However, the time-consuming nature of discrete-event simulation in complex manufacturing systems makes it difficult to deal with large-scale optimization problems. To address this issue, an improved multi-fidelity simulation-based optimization method is proposed and applied in a digital twin-based aircraft parts production workshop, showing better performance than other simulation-based optimization methods.
In recent years, the literature has paid considerable attention to digital twin technology for the implementation of Industry 4.0 and intelligent manufacturing. Most of the literature argues that simulation models are a key platform for digital twins and considers discrete-event simulation to be a suitable method to model real dynamic manufacturing systems. However, the discrete-event simulation of complex manufacturing systems is a time-consuming process. Therefore, it is difficult to deal with the large-scale discrete optimisation problems in digital twin shop floors. To bridge this research gap, we propose an improved multi-fidelity simulation-based optimisation method based on multi-fidelity optimisation with ordinal transformation and optimal sampling (MO2TOS) in the current research. The proposed method embeds heuristic algorithms to accelerate the solution space search efficiency in MO2TOS. Moreover, we develop an improved multi-fidelity simulation-based optimisation system by integrating the proposed method with discrete-event simulation tools and apply this system to a digital twin-based aircraft parts production workshop. Based on this digital twin shop floor, we conduct different production planning experiments to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed improved multi-fidelity simulation-based optimisation method is well-applied in solving large-scale problems and outperforms other simulation-based optimisation methods.
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