SVR and GA Aided Lean Six Sigma Method for Planning in Modular Construction
Bibliographic record
Abstract
Modular construction presents a strong alternative to traditional construction, offering advantages such as improved productivity, and better quality.However, the prefabrication of module components follows a make-to-order process, resulting in customized module components.This design customization, along with various factors such as worker skill levels, and defects in shop drawings causes significant variability in the process times for prefabricating module components at workstations.This variability leads to imbalanced production line, and idle time at workstations, which increases the overall completion time of fabricating module components.To address these challenges, this paper develops a Lean Six Sigma based method that comprises three modules.In the first module, the production line that requires improvements is identified and project objectives are defined.In the second module, the process time data of module components at workstations are collected to identify and analyse inefficiencies in the production line utilizing six sigma performance metrics.The third module focuses on improving and controlling the production line process using support vector regression (SVR) and meta-heuristic optimization.A light gauge steel (LGS) wall panel production line in Edmonton, Canada was analysed to demonstrate the use of the developed method and test its performance.The results show that, after addressing the production line bottlenecks, the sigma level improves to 1.85 𝝈 compared to 1.41 𝝈 earlier.This method can help production managers identify wastes and bottlenecks in the production line, enabling them to plan their processes more efficiently.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".