Multi-dimensional optimization model for schedule fast-tracking without over-stressing construction workers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fast-tracking is an important process to speed the delivery of construction projects. To support optimum fast-tracking decisions, this paper introduces a generic schedule optimization framework that integrates four schedule acceleration dimensions: linear activity crashing; discrete activity modes of execution; alternative network paths; and flexible activity overlapping. Because excessive schedule compression can lead to space congestion and overstressed workers, the optimization formulation uses specific variables and constraints to prevent simultaneous use of overlapping and crashing at the same activity segment. To handle complex projects with a variety of milestones, resource limits, and constraints, the framework has been implemented using the constraint programming (CP) technique. Comparison with a literature case study and further experimentation demonstrated the flexibility and superior performance of the proposed model. The novelty of the model stems from its integrated multi-dimensional formulation, its CP engine, and its ability to provide alternative fast-track schedules to strictly constrained projects without overstressing the construction workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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