Extension of some project scheduling heuristics and their comparison at low and high levels of resource requirement
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
Some of the most frequently used scheduling heuristics for resource constrained projects are Activity Time (ACTIM), Activity Resource (ACTRES) and Resource Over Time (ROT) which are based on Brook's Algorithm (BAG). These heuristics assign resources based upon the priority values of the activities that can be scheduled. In the first part of this study, these heuristics have been modified such that when more than two activities are allowed to be assigned, depending upon the priority rule, that activity is assigned first overriding the priority rule, which, if assigned, will result in minimum resource idle time (MRIT). MRIT is found to improve the performance of these existing heuristics. The second part of the study investigates the performance of these heuristics at high and low levels of resource requirement by each activity. ACTIM was found to perform better than other heuristics at the low level. At the high level, all the heuristics performed equally well.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.005 |
| 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