The impact of project characteristics on the efficiency of activity overlapping in project scheduling
Why this work is in the frame
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Bibliographic record
Abstract
This paper tackles the project scheduling problem in presence of complex networks of activities, resource constraints, overlapping and rework. The objective is to analyse the influence of project characteristics, such as project size, resource constraints, overlapping opportunities and rework, on the efficiency of overlapping in terms of reduction of the project makespan. An exact solution procedure and a metaheuristic are thus proposed to minimize the project makespan, while limiting the use of overlapping. A two-part model is used to conduct a statistical analysis of the influence of project characteristics on the makespan gain with overlapping. Results suggest that the best overlapping decision should consist in overlapping few pairs of overlappable activities with a large degree of overlapping. Furthermore, for complex projects, overlapping decisions should not rely solely on the criticality of the activities. These findings provide a better understanding of overlapping decisions and should guide planners in improving existing practices.
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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 | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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