A constructive and a local search method applied to the examination timetabling 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
This thesis addressed the Examination Timetabling Problem, in particular the Toronto and International Timetabling Competition problems which are mostly used as benchmark instances for comparison of different methods. Both Toronto and International Timetabling Competition problems were, described in detail. Although International Timetabling Competition problems were more complex, they could not be used to describe the Toronto problems. Moreover, a mathematical formulation was also presented for both of these problems. We proposed a new Unified model that encapsulated both the Toronto and the International Timetabling Competition models. A mathematical formulation was also presented for this new model. We also proposed a constructive heuristic approach based on Choquet integral. We used this method to combine the information given by different basic heuristics. We used a fuzzy measure to model the importance of each heuristic as well as the interaction between them. A new set function was also proposed. It was proven that this new set function was in fact a fuzzy measure. We also proposed to use the Differential Evolution algorithm to find good fuzzy measures which then were used in the aforementioned construction algorithm. The Differential Evolution used the new proposed set function to overcome some issues related with the tuning of fuzzy measures. Lastly, we described a new discrete Particle Swarm Optimisation algorithm. The algorithm used kempe-chain.based jumps to move the particles between different solutions. All the proposed approaches were tested using the Toronto and International Timetabling Competition instances.
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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 | 0.025 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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