Methodology for Crew-Job Allocation Optimization in Project and Workface Scheduling
Why this work is in the frame
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Bibliographic record
Abstract
Existing resource scheduling methodologies are insufficient for controlling workflows for individual craft persons in project and workface planning. In practice, the workflow of an individual resource is assigned by a project manager in consideration of the resource supply and resource demand for particular time periods of the project duration. This research study proposes a crew-job allocation methodology to facilitate scheduling and resource management at both project and workface levels. We propose the use of a mathematical model to formulate and solve for the identified problem factoring in resource-time tradeoff options on individual activities. Then a crew-job interaction table is instrumental in visualizing the optimum resource-loaded schedule, which features the shortest project duration and the leanest resource supply under time-dependent resource constraints. Case studies are given to illustrate application of the proposed methodology. This technique potentially provides analytical decision support for not only making cost-effective resource-loaded schedules, but also facilitating the controllability of schedule execution.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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