Aerial refueling scheduling of multi-receiver and multi-tanker under spatial-temporal constraints for forest firefighting
Bibliographic record
Abstract
Forest fires pose a significant threat to human life and property, so the utilization of unmanned aircraft systems provides new ways for forest firefighting. Given the constrained load capacities of these aircraft, aerial refueling becomes crucial to extend their operational time and range. In order to address the complexities of firefighting missions involving multi-receiver and multi-tanker deployed from various airports, first, a fuel consumption calculation model for aerial refueling scheduling is established based on the receiver path. Then, two distinct methods, including an integrated one and a decomposed one, are designed to address the challenges of establishing refueling airspace and allocating tasks for tankers. Both methods aim to optimize total fuel consumption of the receivers and tankers within the aerial refueling scheduling framework. The optimization problem is established as nonlinear optimization models along with restrictions. The integrated method seamlessly combines refueling rendezvous point scheduling and tanker task allocation into unified process. It has a complete solution space and excels in optimizing total fuel consumption. The decomposed method, through the separation of rendezvous point scheduling and task allocation, achieves a reduced computational complexity. However, this comes at the cost of sacrificing optimality by excluding specific feasible solutions. Finally, numerical simulations are carried out to verify the feasibility and effectiveness of the proposed methods. These simulations yield insights crucial for the practical engineering application of both the integrated and decomposed methods in real-world scenarios. This comprehensive approach aims to enhance the efficiency of forest firefighting operations, mitigating the risks posed by forest fires to human life and property.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".