Machine Learning-Driven Truck–Drone Collaborative Delivery for Time- and Energy-Efficient Last-Mile Deliveries
Bibliographic record
Abstract
Truck–drone collaboration in urban last-mile deliveries offers an innovative solution to address inefficiencies in modern supply chain networks. This work leverages real drone flight data to train a machine learning-based drone energy model that accurately estimates the time and energy consumption of drones to support resource-related decisions. An AI engine is proposed that integrates the drone energy model with a self-organizing feature map algorithm, ensuring continuous drone operation without reliance on charging infrastructure. A total of 93 comprehensive scenario-based simulations over 1 week of delivery data in MATLAB offers actionable insights into resource allocation, demonstrating that deploying three drones at five truck stops results in the most energy-efficient delivery scenario, reducing energy consumption by 36% compared to the least efficient outcome, in which a single drone is deployed at four stops. The holistic and data-driven approach to truck-drone collaboration presented in this work has the potential to bridge the gap between theoretical models and real-world applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".