Extension of some project scheduling heuristics and their comparison at low and high levels of resource requirement
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".