An evolutionary optimization method to determine optimum degree of activity accelerating and overlapping in schedule compression
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Bibliographic record
Abstract
Compressing project schedule using activity accelerating and overlapping requires that an intensive time–cost trade-off analysis be carried out, to determine costs and benefits for each day of compression. However, the cost elements and implications of compression techniques differ significantly, since activity accelerating imposes extra direct cost whereas activity overlapping adds a risk of changes and rework. Such a trade-off becomes even more complicated in capital projects comprised of a large number of schedule activities and relationships. The variety of combinations of accelerating and overlapping of different activities in these complex networks can offer numerous possibilities for compression with various costs and potential risks. The lack of a reliable analytical tool for performing a precise cost-benefit analysis causes this critical task to be performed in a subjective manner during the planning stage of projects. The purpose of this paper is to present an advanced method using a multi-objective evolutionary optimization tool seeking the optimum degree of accelerating and overlapping during the schedule compression process. This optimization technique would be beneficial in maximizing project benefits while meeting the intended target dates.
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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 | 0.001 | 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 | 0.001 |
| 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