Discrete Event Simulation-Driven Method Solving Permutation Flowshop Scheduling Problem in Digital Twins
Digital twin technology is becoming increasingly vital in intelligent production and Industry 4.0. Nevertheless, the simulation system of the digital twin frequently serves merely as a virtual representation of the actual system, neglecting its significant analytical and decision-making capabilities...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10883960/ |
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| Summary: | Digital twin technology is becoming increasingly vital in intelligent production and Industry 4.0. Nevertheless, the simulation system of the digital twin frequently serves merely as a virtual representation of the actual system, neglecting its significant analytical and decision-making capabilities. This research introduces a discrete-event simulation-based digital twin optimization methodology to determine the optimal job sequence for the permutation flowshop scheduling problem (PFSP). The concept of sequence-dependent recovery time (SDRT) is brought into the PFSP for the first time, and the four subproblems of PFSP with SDRT was proposed. The computational simulation findings on a benchmark dataset indicate that the SDRT substantially influences optimization, enhancing average scheduling performance by around 14% relative to integrating recovery time into processing time. The empirical case study findings indicate that the suggested simulation-optimization strategy decreases completion time by 0.95% relative to the conventional mathematical approach while addressing the actual PFSP using SDRT. These findings validate the effectiveness of discrete event simulation-based digital twin technology for addressing PFSP with SDRT. |
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| ISSN: | 2169-3536 |