Solving Toronto Examination Timetabling Using Heuristic Method
Bibliographic record
Abstract
The examination timetabling problem has attracted the interested of many researchers over the years. However, this problem is difficult to solve due to the lack of benchmark dataset and many constraints that need to be satisfied in examination timetabling problem. Toronto benchmark data contains 13 real-world examination timetabling problem which have different conflict density for every dataset. Many researchers solved Toronto benchmark data using different method in order to produce a timetable which is feasible and solve all the constraints. To produce a feasible examination timetable, all the exams need to be scheduled into timeslot while satisfying the hard constraint and soft constraint. The timetable result should have the minimum penalty value in term of spread exams. Therefore, the technique partial graph heuristic with hill climbing method should be implemented to solve Toronto examination timetabling problem. The graph heuristic method will partially schedule the exam and then improved by hill climbing method. This process will be repeated until all the exams are scheduled. By using this technique, the solution of timetable result can comply all of the constraints and has a competitive result compared to other researchers' result.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".