Devising extended-duration schedules of enhanced resource leveling
Bibliographic record
Abstract
The use of resource management techniques is crucial to resolve conflicts and achieve the efficient utilization of resources. Particularly, resource-leveling techniques schedule activities in unconstrained-resource conditions to minimize fluctuations in resource profiles. According to the literature, resource leveling has typically been performed by considering that the original project duration remains fixed. Virtually, some extension in the project duration might be acceptable should a considerable enhancement in resource leveling be achieved. This paper enhances resource leveling through devising schedules of extended duration that exhibit resource profiles of lower fluctuation. Critical path method (CPM) networks of extended total floats are utilized to provide expanded yet definite spaces to search for schedules of lower resource fluctuation. The modified CPM networks accommodate for employing optimization models and searching optimal or near-optimal solutions. For demonstration, a genetic algorithm model was formulated to solve two case-study networks of 30 and 120 activities. The results indicate that schedules of lower fluctuation in resource profiles were obtained beyond the original networks’ duration.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".