Iterated local and very-large-scale neighborhood search for a novel uncapacitated exam scheduling model
Bibliographic record
Abstract
We consider a novel uncapacitated exam scheduling model where the soft constraint stipulates that the exams should be spread over the periods so as to avoid adjacent conflicts as much as possible. The proposed method starts by constructing an initial feasible timetable. A two-phase method is then iterated. The first phase is a local search (LS) heuristic where two neighborhoods, ExamShift and KempeSwap, are searched using the token-ring strategy. The second phase takes as input the local optimum obtained during the first phase and performs a very-large-scale neighborhood (VLSN) search. We prove that searching the exponential neighborhood is NP-hard. Hence a polynomial-time heuristic based on reordering the periods is proposed for exploring a polynomial sized part of the neighborhood. The incumbent of the VLSN search is then perturbed and the method iterated. The practicability and effectiveness of our approach is studied by testing it on the university of Toronto benchmark instances and comparing it to an established method adapted to the new model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".