Uncertainty-Aware Linear Schedule Optimization: A Space-Time Constraint-Satisfaction Approach
Why this work is in the frame
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Bibliographic record
Abstract
Schedules and physical workspaces are two key elements of linear construction projects that are extremely interdependent. Any negligence in incorporating spatial and temporal constraints in developing and improving schedules of linear projects results in inevitable delays and workspace congestions and can substantially hinder the performance of the activity resources. This study augments the current linear scheduling methods by presenting an uncertainty-aware optimization framework to optimize the duration of linear projects while minimizing their potential congestions. The methodology is built upon the new concept of space-time float for explicit consideration of spatio-temporal constraints of activities and their inherent uncertainty. A constraint satisfaction approach was used for the two-tier optimization of duration and congestion. A fuzzy inference system was also incorporated to assess the inherent uncertainty in the schedule. Two case examples from literature are analyzed. The results demonstrate the effectiveness of the proposed method in planning and control of the unforeseen variations from planned schedules of linear projects.
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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 | 0.000 |
| 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 | 0.000 |
| 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