An efficient implementation of a VNS heuristic for the weighted fair sequences problem
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
Abstract In the weighted fair sequences problem (WFSP), one aims to schedule a set of tasks or activities sthat the maximum product between the largest temporal distance between two consecutive executions of a task and its priority is minimized. The WFSP covers a large number of applications in different areas, ranging from automobile production on a mixed‐model assembly line to the sequencing of interactive applications to be aired in a digital TV environment. This paper proposes an iterative heuristic method for the WFSP centered on an efficient implementation of a variable neighborhood search heuristic. Computational experiments on benchmark instances show that the proposed metaheuristic outperforms the state‐of‐the‐art method proposed to the problem, obtaining comparable solution values in much less computational time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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