An Enhanced Scheduling Technique for Modular Construction Manufacturing
Bibliographic record
Abstract
Modular construction manufacturing (MCM) is potentially built through a more efficient and cost-effective method compared to the on-site construction practice. The increased interest in manufacturing of the building construction process demands special methods of design and manufacturing to improve production efficiency. MCM provides opportunities to apply Lean for production efficiency in the plant, including eliminating waste and supporting the delivery of customized products in a shorter time and at a lower cost. Lean is a concept first developed in the manufacturing industry which has been since adapted to the construction industry. Although the focus of Lean in both industries is the same, Lean tools vary between manufacturing and construction since these two industries differ in nature. Lean as the concept is applicable to any industries, taking into consideration that MCM has characteristics of both manufacturing and construction yet is distinct and should be seen in the class of its own. Given the distinct nature of MCM, the technical elements in “Lean production” and “Lean construction” are not sufficient to achieve the Lean goals for MCM industry, necessitating a modified framework by which to exploit the potential benefits of modular building. This paper provides a deeper understanding of the modular construction manufacturing and the difference between the manufacturing, construction, and MCM industries. The focus of this paper is to adopt an enhanced scheduling technique which can adequately fulfill the production efficiency demands based on particular characteristic of modular construction manufacturing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".