A Simulation Model to Analyze Different Automation Scenarios in a Mixed-Assembly Manufacturing Line: Timber-Frame Prefabrication Industry
Bibliographic record
Abstract
As prefabrication of timber structures in the construction sector becomes part of the solution to overcome the housing and environmental crisis, the lack of efficiency of this industry needs to be addressed. This inefficiency, characterized by low production capacity, high costs, and large workforce, are in part due to the low adoption of automation and robotic technologies, caused by insufficient knowledge and research measuring the impact of these technologies. This paper aims to measure the efficiency of introducing automation in a prefabrication environment of timber-frame structures, through a decision analysis tool. Four layout types with varying automation levels are explored using simulation modeling, while KPIs are exploited to measure the efficiency of each production environment. A sensitivity analysis is conducted to assess the most performing layout if a new type of wall, requiring fewer tasks, is introduced in the product mix. A design of experiments (DOE) is also performed on the fourth layout, a fixed robotic cell with the highest level of automation, to assess the most influential variables. Results show a positive increase in worker, space, and production efficiency as the level of automation increase. This research contributes to the body of knowledge by providing a decision analysis tool supporting the choice of an optimized layout for the prefabrication of timber-frame walls (TFW). The findings and the decision analysis tool should provide solid grounds for future exploration of automation and practice in the field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".