Bonggwon Kang, PhD

Postdoctoral Scholar jointly affiliated with the Department of Industrial & Systems Engineering, University of Washington, and the Research Institute of Intelligent Logistics Big Data, Pusan National University.

E-mail: bonggwon.kang@gmail.com

I'm a postdoctoral scholar in the the Department of Industrial & Systems Engineering at the University of Washington, USA. I received my Ph.D. and B.S. in Industrial Engineering from Pusan National University in 2024 and 2019, respectively. My research focuses on uncertainty quantification for sustainable digital twins in complex manufacturing and material handling systems.

Methodological Frameworks: Discrete-event simulation, Surrogate modeling, Bayesian statistics, Gaussian process regression
Industrial Applications: Semiconductor/display fabs, container terminals, distribution centers, and automobile assembly lines

Highlight I: Simulation Modeling & Analysis

We develop simulation models that capture the underlying mechanisms of real-world operations and generate quantitative insights for complex production and logistics systems. These models provide a rigorous foundation for digital twin research, enabling studies in tactical facility design, operational planning, and real-time control. Across these applications, we integrate mathematical optimization and machine learning to support model validation and decision analysis.

Small Size FAB Simulation.mp4

3D SVSRS.mp4

반도체생산시스템.mp4

Highlight II: Simulation Optimization

In semiconductor manufacturing, efficient material handling is essential for sustaining high throughput in complex fab operations. We developed a detailed AMHS simulation model and used it to evaluate performance across configurations and scenarios, identifying bottlenecks in material handling operations. Building on these insights, we proposed a simulation-based optimization framework for proactive vehicle dispatching for future transport demand. Extensive simulation experiments demonstrated improved throughput and reduced vehicle travel time, confirming robustness under stochastic conditions. This work was published in IEEE Transactions on Automation Science and Engineering and presented at the 2023 INFORMS Annual Meeting (Phoenix, AZ, USA).

[3D] FAB AMHS Animation 3.mp4

[3D] FAB AMHS Animation 2.mp4

Highlight III: Simulation Calibration

In modern semiconductor manufacturing, system simulation increasingly support planning and control, yet their predictive accuracy can deteriorate under uncertainty, creating gaps between simulated and realized performance and biasing decisions. To mitigate this issue, we developed a Bayesian calibration framework that learns and corrects model discrepancy using limited real-world data, substantially improving predictive fidelity even under congested operating regimes. This work was published in the Journal of Manufacturing Systems and presented at the 2024 INFORMS Annual Meeting, with ongoing efforts to extend the framework to broader smart manufacturing applications.

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