An integer programming model and heuristic algorithm for automatic scheduling in synchrotron facilities
Bibliographic record
Abstract
This paper studies the automatic scheduling problem at the Canadian national synchrotron facility, Canadian Light Source (CLS). An automatic scheduling tool needs to be developed to replace the current manual approach for scheduling experiments on a set of beamlines - resources that generate high-intensity X-rays for use in many kinds of scientific experiments. We present an, Integer programming model for this scheduling activity by formulating it as a problem of unrelated and paralleled machines with partially overlapping capabilities. Furthermore a heuristic based approach is used that can save computation time by pruning the search space. Using realistic data sets generated using parameters made available by CLS, we compare the performance of the base line approach that uses ILOG CPLEX implementation of the Integer programming algorithm with one that uses heuristics. The results show that the heuristic approach runs faster than the base-line, but at the cost of producing a less optimal scheduling solution. An obvious advantage of the study presented in this paper is that the automatic scheduling can handle more scheduling conditions and constraints than humans are able to handle manually and can reach optimal solutions. As far as we know, this is the first attempt to propose an automatic scheduling approach for synchrotron facilities like CLS around the world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".