Integrated Simulation and Lean Approach for Production Line Improvement in a Prefabricated Panelized Homebuilding Facility
Bibliographic record
Abstract
The construction industry is increasingly adopting off-site construction to achieve better quality buildings, to reduce the environmental impact of construction activities, and to attain less schedule variability.When shifting the construction process to a factory, the project is less vulnerable to uncertainties, such as unexpected weather conditions, labour turnover, and material delivery disturbances.Panelized construction is a method in which walls, floors, and roofs are built-in panels at the factory and shipped for on-site assembly.This paper describes the simulation of a production line in a panelized modular home manufacturing facility with the aim of better understanding and improving the production processes associated, in particular, with the first phase of production, namely the multiwall panel production line.Discrete event simulation (DES) is used to investigate and analyze the existing facility processes in terms of production time.The goal is to enhance productivity, reduce work-in-progress, and decrease idle time.The panelized manufacturing facility in the presented study produces dozens of multiwall panels per day, ranging in length from 3 to 13 meters, and both interior and exterior walls are produced on the same production line, each having different physical properties.Applying lean concepts and philosophy, the simulation tool is used to explore various scenarios where the idle time can be identified and minimized as much as possible from a practical perspective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".