Optimising vertical deliveries with integrated hybrid drone-truck systems
Bibliographic record
Abstract
Last-mile delivery (LMD) constitutes a significant portion of contemporary distribution networks, accounting for up to 75% of total supply chain costs. Drones offer numerous advantages over conventional delivery systems, including reduced energy consumption, enhanced flexibility, and lower carbon emissions. However, drone delivery faces limitations such as range, payload carrying capacity, battery life, and regulatory hurdles. This paper presents a novel optimisation model for a 3D multi-visit multi-launch integrated hybrid drone–truck delivery system capable of delivering to multiple customers at various altitudes. Drones can launch multiple times from a truck stop and rendezvous with the truck at a subsequent truck stop. A formulation is proposed that incorporates real-time payload, payload-carrying capacity, battery capacity, and energy consumption profiles to determine optimal truck and drone routes. Small-scale instances are solved to optimality using Gurobi, while large-scale instances are addressed through a solution approach integrating a Relax-&-Fix heuristic and column generation. Experiments are conducted to demonstrate the model's key features and emphasise the significance of incorporating multi-drone launches and their energy consumption function into the delivery decision process. Numerical results indicate trade-offs between drone and truck travel time, customer altitude, and drone energy consumption.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".