Conference Article
Denisse Valenzuela Robles, Souravik Dutta, Rafiq Ahmad
Proceedings of the 16th Conference on Learning Factories (CLF 2026), 2026
Proceedings of the 16th Conference on Learning Factories (CLF 2026), 2026
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Cite
APA
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Robles, D. V., Dutta, S., & Ahmad, R. (2026). Design and Simulation-Based Optimization of a Virtual Learning Factory for Structural Steel Prefabrication.
Chicago/Turabian
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Robles, Denisse Valenzuela, Souravik Dutta, and Rafiq Ahmad. “Design and Simulation-Based Optimization of a Virtual Learning Factory for Structural Steel Prefabrication.” Proceedings of the 16th Conference on Learning Factories (CLF 2026), 2026.
MLA
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Robles, Denisse Valenzuela, et al. Design and Simulation-Based Optimization of a Virtual Learning Factory for Structural Steel Prefabrication. 2026.
BibTeX Click to copy
@conference{denisse2026a,
title = {Design and Simulation-Based Optimization of a Virtual Learning Factory for Structural Steel Prefabrication},
year = {2026},
series = {Proceedings of the 16th Conference on Learning Factories (CLF 2026)},
author = {Robles, Denisse Valenzuela and Dutta, Souravik and Ahmad, Rafiq}
}
Abstract
Off-site prefabrication of structural steel building components offers improved productivity, safety, and quality; however, optimizing production flow and workforce training remains challenging. This study presents the design and simulation-based optimization of a Virtual Learning Factory (VLF) developed to model and enhance structural steel prefabrication while supporting postgraduate Lean Manufacturing education. A representative steel building was first designed to define component families and production requirements. The manufacturing workflow – comprising cutting, drilling, assembly, welding, cleaning, and galvanizing – was modeled using Discrete Event Simulation (DES) under deterministic processing conditions. Iterative, Key Performance Indicator (KPI)-driven layout redesigns were implemented following lean principles, including line balancing, operation integration, flow synchronization, and controlled material release. System performance was evaluated using lead time, throughput, work-in-progress, and station utilization. Results show a significant reduction in total production time and substantial increase in throughput, alongside bottleneck mitigation, reduced queue accumulation, and improved flow continuity. Notably, performance gains were achieved without altering station cycle times, confirming the impact of layout and capacity configuration rather than process acceleration. Beyond operational analysis, the VLF is positioned as a didactic platform for experiential learning, enabling students to analyze production systems, implement lean interventions, and validate outcomes through simulation. The framework establishes a scalable foundation for integrating digital manufacturing research, education, and future simulation-optimization extensions.