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All Journal International Journal of Technology and Modeling
Karahan, Yasemin
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Sparse System Dynamics Modeling for High-Dimensional Decision-Making in Industrial Automation Kara, Hasan; Karahan, Yasemin; Uysal, Cengiz
International Journal of Technology and Modeling Vol. 4 No. 1 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i1.126

Abstract

The increasing complexity of industrial automation systems has introduced significant challenges in modeling and analyzing high-dimensional decision-making environments. Traditional system dynamics (SD) models often struggle with scalability and computational efficiency when faced with numerous interdependent variables and feedback loops. In this study, we propose a Sparse System Dynamics Modeling (SSDM) approach that leverages sparsity-aware techniques to identify and retain only the most influential causal relationships within complex industrial systems. The SSDM framework introduces a structure reduction mechanism based on variable correlation thresholds and influence-weight pruning, enabling the construction of lightweight yet expressive models. By applying this method to a case study involving automated production line optimization, we demonstrate that SSDM maintains the predictive integrity of full-scale SD models while reducing computational overhead by up to 60%. The model also facilitates faster scenario simulations and more interpretable decision pathways, making it suitable for real-time industrial planning and control. Our results highlight the potential of sparse modeling in addressing the curse of dimensionality in industrial environments, providing a scalable and interpretable alternative for decision-makers in smart manufacturing and Industry 4.0 applications.
Co-Authors Kara, Hasan Uysal, Cengiz