Minimizing customer waiting time in drone delivery systems: An optimization approach considering heterogeneous fleets and package setup time using modified coot algorithms
Bibliographic record
Abstract
This study addresses the drone delivery problem with a unique focus on minimizing total customer waiting times, considering the heterogeneous nature of drones and the setup times required for loading customer demands. Unlike traditional routing problems that prioritize cost and route optimization, this research emphasizes timely deliveries, which are critical in both commercial and humanitarian applications. The study introduces two mathematical models and four versions of the coot optimization algorithm, including three modified variants and one classical version. These algorithms incorporate new movement mechanisms, enhanced leader selection strategies, and adaptations of the regenerating strategy to efficiently solve the drone delivery problem. Computational experiments reveal that one modified coot optimization algorithm significantly outperforms the classical version, offering valuable insights into both coot optimization literature and the drone delivery problem. By emphasizing the importance of timely deliveries, this research provides effective solution strategies applicable to both commercial and humanitarian contexts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".