Communication-Aware Drone Delivery Problem
Bibliographic record
Abstract
The drone delivery problem (DDP) has been introduced to include aerial vehicles in last-mile delivery operations to increase efficiency. However, the existing studies have not incorporated the communication quality requirements of such a delivery operation. This study introduces the communication-aware DDP (C-DDP), which incorporates handover and outage constraints into the conventional multi-depot multi-trip green vehicle routing problem with time windows. In particular, any trip of a drone to deliver a customer package must require less than a certain number of handover operations and cannot exceed a predefined outage duration threshold. A mixed integer programming (MIP) model is developed to minimize the total flight distance while satisfying communication constraints. We present a genetic algorithm (GA) that can solve large instances and compare its performance with an off-the-shelf MIP solver. Computational study shows that the GA and MIP solver performances are equivalent to solving smaller instances. We also compare the GA performance against another evolutionary algorithm, particle swarm optimization (PSO), for larger instances and find that the GA outperforms the PSO with slightly longer CPU times. The results indicate that ignoring the communication constraints would cause significant operational disruption risk and this risk can be easily mitigated with a slight sacrifice from flight distances by incorporating the proposed communication constraints. In particular, the communication performance can be improved by up to 28.9% when the flight distance is increased by 19.1% at most on average.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".