An Iterative Two-Phase Optimization Method Based on Divide and Conquer Framework for Integrated Scheduling of Multiple UAVs
Bibliographic record
Abstract
Task scheduling of multiple UAVs has become a highly active area of research in recent years. Previous research has generally solved the problem in a whole manner, which makes it hard to efficiently generate high-quality task scheduling schemes due to prohibitive computational complexity. By contrast, the paper constructs a novel divide and conquer framework for multi-UAV task scheduling (DCF), which partitions the original multi-UAV scheduling problem into multiple scheduling sub-problems for all the UAVs. To be specific, DCF includes two phases: one is the task allocation phase which produces multiple scheduling sub-problems and the other is the single UAV scheduling phase which generates the scheduling scheme with sequential tasks for each single UAV considering constraints involving UAV capabilities and task demands. Two phases are iteratively performed until the predefined stopping criteria are met. In the task allocation phase, we propose a tabu-list-based simulated annealing (SATL) algorithm to realize task allocation among multiple UAVs. After obtaining the task allocation scheme, a satisfactory scheduling scheme of each single UAV is generated by variable neighborhood descent (VND) algorithm. Extensive experiments and comparative studies are conducted, demonstrating the efficiency of DCF and the proposed SATL-VND algorithm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".