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Discrete Event Simulation-Driven Method Solving Permutation Flowshop Scheduling Problem in Digital Twins
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DOI：10.1109/ACCESS.2025.3541223.png)
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Abstract
En 中文
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.
Keywords:
Digital twins
Production facilities
Job shop scheduling
Data models
Decision making
Processor scheduling
Optimization methods
Discrete event simulation
Computer numerical control
Time factors
permutation flowshop
recovery time
digital twins