TY - GEN
T1 - Hyperparameter Tunning in Simulation-based Optimization for Adaptive Digital-Twin Abstraction Control of Smart Manufacturing System
AU - Seok, Moon Gi
AU - Tan, Wen Jun
AU - Su, Boyi
AU - Cai, Wentong
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/6/8
Y1 - 2022/6/8
N2 - Smart manufacturing utilizes digital twins (DTs) that are virtual forms of their production plants for optimizing decisions. Discrete-event models (DEMs) are frequently used to model the production dynamics of the plants. To accelerate the performance of the discrete-event simulations (DES), adaptive abstraction-level conversion (AAC) approaches were proposed to change specific subcomponents of the DEM with corresponding abstracted queuing models during the runtime based on the steady-state of the DEMs. However, the speedup and accuracy loss of the AAC-based simulations (ABS) are highly influenced by user-specified significance level α (degree of tolerance of statistical invariance between two samples) and the stability of the DEMs. In this paper, we proposed a simulation-based optimization (SBO) that optimizes the problem based on genetic algorithm (GA) while tuning the hyperparameter (α) during runtime to maximize the speedup of ABS under a specified accuracy constraint. For each population, the proposed method distributes the computing budget between the α exploration and fitness evaluation. A discrete-gradient-based method is proposed to estimate each individual's initial α (close to the final optimum) using previous exploration results of neighboring individuals so that the closeness can reduce the iterative α exploration as GA converges. We also proposed a clean-up method that removes inferior results to improve the α estimation. The proposed method was applied to optimize raw-material releases of a large-scale manufacturing system to prove the concept and evaluate the performance under various situations.
AB - Smart manufacturing utilizes digital twins (DTs) that are virtual forms of their production plants for optimizing decisions. Discrete-event models (DEMs) are frequently used to model the production dynamics of the plants. To accelerate the performance of the discrete-event simulations (DES), adaptive abstraction-level conversion (AAC) approaches were proposed to change specific subcomponents of the DEM with corresponding abstracted queuing models during the runtime based on the steady-state of the DEMs. However, the speedup and accuracy loss of the AAC-based simulations (ABS) are highly influenced by user-specified significance level α (degree of tolerance of statistical invariance between two samples) and the stability of the DEMs. In this paper, we proposed a simulation-based optimization (SBO) that optimizes the problem based on genetic algorithm (GA) while tuning the hyperparameter (α) during runtime to maximize the speedup of ABS under a specified accuracy constraint. For each population, the proposed method distributes the computing budget between the α exploration and fitness evaluation. A discrete-gradient-based method is proposed to estimate each individual's initial α (close to the final optimum) using previous exploration results of neighboring individuals so that the closeness can reduce the iterative α exploration as GA converges. We also proposed a clean-up method that removes inferior results to improve the α estimation. The proposed method was applied to optimize raw-material releases of a large-scale manufacturing system to prove the concept and evaluate the performance under various situations.
KW - Abstraction-level conversion
KW - discrete-event models
KW - genetic algorithm
KW - hyperparameter tuning
KW - manufacturing systems
KW - simulation-based optimization
UR - http://www.scopus.com/inward/record.url?scp=85132440119&partnerID=8YFLogxK
U2 - 10.1145/3518997.3531024
DO - 10.1145/3518997.3531024
M3 - Conference contribution
AN - SCOPUS:85132440119
T3 - ACM International Conference Proceeding Series
SP - 61
EP - 68
BT - Proceedings of the 2022 ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation, ACM SIGSIM PADS 2022
PB - Association for Computing Machinery
T2 - 2022 ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation, SIGSIM PADS 2022
Y2 - 8 June 2022 through 10 June 2022
ER -