Two Stages Best First Search Algorithm Using Hard and Soft Constraints Heuristic for Course Timetabling
Bibliographic record
Abstract
Course Timetabling is to combine the components of teachers, students, subjects, and time. The schedule consists of days on the horizontal axis and time of the clock on the vertical axis. The best first search algorithm is an algorithm to find a solution from existing nodes. Nodes can be various types of problems. In this case, the node is a two-dimensional schedule. In course timetabling there are several constraints or called heuristic functions that must be calculated. The Heuristic function consists of two parts. The first part is a constraint that must be fulfilled (Hard Constraint). There is a schedule of conflicts of the demands of the teacher cannot teach at a certain time. The second part is a constraint which is an optimization to make the search results better in heuristic value (Soft Constraint). Student schedules and teachers are worked out sequentially so students do not wait too long. Best First Search algorithm is designed in two stages. The first step is to find the first heuristic value that must be fulfilled. The second step is to find the second heuristic value. The quality of the solution obtained is between 40% -75%. The significance of this research is that dividing the Best First Search algorithm into two stages yields advantages in terms of meeting hard constraints and the time needed to process the algorithm better.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".