Enhanced Resource Scheduling Framework For Industrial Construction Projects
Why this work is in the frame
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Bibliographic record
Abstract
The time and cost required to optimize resource assignments in industrial construction is exacerbated by the size, complexity, and specialized requirements of these projects. This study introduces an automated, simulation-based scheduling method to enhance, accelerate, and facilitate variable resource allocation in industrial construction. The proposed framework links a time-stepped simulation engine to an integrated database management system containing project information and historical data. The developed system auto-generates an efficient schedule respecting project constraints and uncertainties, such as limited resource availability and variable labor resources based on historical data, calendars, and shifts. Graph theory algorithms are used to optimize variable resource allocation in the time-stepped simulation, resulting in the leveling of resource histograms and, consequently, the generation of an efficient project schedule. Applying the proposed framework to an illustrative example demonstrated its capabilities in generating efficient schedules based on variable resource allocation constraints.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it