Combining Decomposition and Hybrid Algorithms for the Satellite Range Scheduling Problems
Bibliographic record
Abstract
Multiple-resource satellite scheduling problem (MuRRSP) is a complex and difficult scheduling problem, which schedules a large number of task requests onto ground-station antennas in order to communicate with the satellites. We first examined several exact algorithms that were previously implemented in the machine scheduling field. They are column generation and logic-based Benders decomposition. A new hybrid approach that combines both column generation and logic-based Benders decomposition is proposed. The hybrid performed well when there is a large number of machines. Next, we presented a connection between the parallel machine scheduling problem and MuRRSP in order to solve MuRRSP with exact algorithms. Furthermore, we proposed a strengthened cut in the sub-problem of the logic-based Benders \ndecomposition. The resulting algorithm proved to be very effective. Barbulescu’s benchmark problems were solved and proved optimal with an average run-time less than one-hour. To the best of our knowledge, previous efforts to solve MuRRSP were all heuristic based and no optimal schedules existed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".