On-demand meal delivery: Drone scheduling with battery replacement optimization
Bibliographic record
Abstract
Drones have become a promising solution for on-demand delivery thanks to their ability to travel fast and navigate without road restrictions. In the context of direct meal delivery using drones, it is common practice to replace the drone’s battery after each round trip between the central launch site and customer locations to prevent power interruptions. This practice can lead to frequent battery replacements, resulting in increased downtime and decreased drone utilization. To enhance the efficiency of drone delivery, we take into account load-dependent energy consumption for the drones and optimize battery replacement along with drone scheduling. A mixed integer programming formulation is constructed to mathematically capture the problem. Additionally, we develop a time-expanded network flow method and a tailored hybrid variable neighborhood search algorithm to solve the problem exactly and heuristically. Computational studies validate the effectiveness and efficiency of the proposed operational model and solution approaches. Our results indicate that optimizing battery replacement can induce an increase in on-time deliveries by up to 7.69% compared to replacing batteries after each return, and by 14.29% compared to only replacing them when energy levels are low. Such benefits are particularly significant in scenarios with tighter delivery deadlines and longer battery replacement times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.006 | 0.001 |
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".