Planning and scheduling bridge girders fabrication through shop-floor operations simulation
Bibliographic record
Abstract
In modular and offsite construction, structural components are prefabricated in a fabrication shop resembling a manufacturing plant in order to accelerate field construction processes. However, the dynamic nature of such fabrication operations often demands frequent adjustments to original production plans so as to fit actual project start-finish schedules in terms of completion dates and budgets and accommodate changes in design details. It is a daunting task to minimize disruptions to ongoing workflows while realizing high efficiency in utilization of shop production resources. In reality, such situations constantly press production manages to take prompt decisions without having analytical decision support in exploring available options, potentially resulting in loss of productivity on the shop floor and missed deadlines. This research introduces a structured approach to communicating shop-floor operations simulation at various management levels. The paper focuses on the representation of project schedules and production plans resulting from simulation in straightforward, role-specific “bar charts”. The applicability of the proposed approach is demonstrated with a case in the setting of a steel girder fabrication shop.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".