Introduction to the multi-skilled resource constrained multi project and multi type scheduling problem
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we extend the multi-skilled resource-constrained multi-project scheduling problem (MSRCMPSP) by introducing the concept of multiple project types (MSRCMPMTSP). The study considers two categories of projects: investment projects, which generate positive net present values (NPVs), and mandatory projects, which result in negative NPVs but are required to be executed. To solve this problem, we employ a priority rule-based heuristic approach. Specifically, forward scheduling is applied to projects expected to yield positive NPVs, whether they are optional or mandatory. In contrast, backward scheduling is used for projects with negative NPVs, as this strategy minimizes the impact of excessive negative NPVs. The dataset for this study is constructed using the design of experiments (DoE) methodology, enabling a comprehensive evaluation of the proposed heuristic. We compare the performance of our approach against randomly generated schedules through extensive simulations. The results indicate that the heuristic is effective in addressing the MSRCMPMTSP.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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