Timetabling Lab Sessions at the Koningsberger Building
Bibliographic record
Abstract
Since 2015 the Victor J. Koningsberger building has been used by multiple departments and faculties of Utrecht University to host their lab sessions. To help with the scheduling of the lab sessions, we created an algorithm to automatically create a timetable based on the requests. In this work we describe how we modeled the problem. We also show our algorithm, which is a version of Simulated Annealing, and its components. Elaborate results are shown of our research to find the best settings for our algorithm. Finally, we also take a look at the GUI application that we created to allow course coordinators to fill in their demands and the central coordinator(s) to create a timetable from those demands, using our algorithm.\nOur method has already been used to create the timetables for the first two quarters of the 2017-2018 academic year, and it has been decided to use these timetables instead of the hand-made ones. More recently, it has been decided that our GUI application and algorithm will be used for the creation of the timetables for the third and fourth quarter of the 2017-2018 academic year as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".