Hyperparameter Tunning in Simulation-based Optimization for Adaptive Digital-Twin Abstraction Control of Smart Manufacturing System

Moon Gi Seok, Wen Jun Tan, Boyi Su, Wentong Cai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2022 ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation, ACM SIGSIM PADS 2022
PublisherAssociation for Computing Machinery
Pages61-68
Number of pages8
ISBN (Electronic)9781450392617
DOIs
StatePublished - 8 Jun 2022
Event2022 ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation, SIGSIM PADS 2022 - Virtual, Online, United States
Duration: 8 Jun 202210 Jun 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2022 ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation, SIGSIM PADS 2022
Country/TerritoryUnited States
CityVirtual, Online
Period8/06/2210/06/22

Keywords

  • Abstraction-level conversion
  • discrete-event models
  • genetic algorithm
  • hyperparameter tuning
  • manufacturing systems
  • simulation-based optimization

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