Coordinated Scheduling of Air and Space Observation Resources via Divide-and-Conquer Framework and Iterative Optimization
Bibliographic record
Abstract
At present, independent scheduling of Earth-observation resources (EORs) is usually difficult to satisfy diverse observation requirements and cannot realize the full potential of space–air resource networks. To utilize EORs comprehensively, this study constructs a divide-and-conquer framework (DCF) for a coordinated scheduling of air and space observation resources (i.e., satellites and unmanned aerial vehicles). The DCF can decompose the original scheduling problem into a task allocation subproblem and multiple task scheduling subproblems that can be solved using a coordination planner and subplanners, respectively. For the task allocation subproblem, we propose a simulated annealing algorithm combined with variable neighborhood adjustment (SA-VNA) method, where a solution variation strategy (SVS) is designed. The SVS iteratively adjusts the task allocation scheme according to the coordinated scheduling result of the last iteration. Based on the allocation scheme, multiple task scheduling subproblems are generated, and existing effective algorithms are used to resolve them. Extensive experiments and comparative analysis show that SA-VNA outperforms several peer algorithms overall, indicating that DCF plus SA-VNA can significantly improve the efficiency of space–air resource networks.
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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".