Framework for truck–RPAS hybrid models in last-mile delivery
Bibliographic record
Abstract
This study develops a hybrid optimization framework integrating remotely piloted aircraft systems (RPASs) with conventional truck delivery networks to enhance last-mile logistics efficiency. To balance operating cost, service time, regulatory risk, and energy usage, a novel multi-objective mixed-integer linear programming model is developed. High-quality Pareto-optimal solutions are produced by the non-dominated sorting genetic algorithm II, which methodically manages trade-offs between the conflicting goals. Risk assessment is embedded using specific operations risk assessment principles, and energy consumption is optimized through dynamic battery management strategies for RPASs. Extensive computational experiments demonstrate that the proposed hybrid truck–RPAS system achieves notable operational improvements compared to traditional truck-only models. The model yields an 8.3% reduction in operational costs, an 8.6% decrease in delivery time, an 11.2% reduction in cumulative risk indices, and a 9.4% decrease in overall battery usage. Convergence analysis and scalability evaluation further confirm the robustness and practical viability of the proposed solution approach. By integrating regulatory compliance, energy sustainability, and operational resilience, this research provides a scalable and adaptable framework for the effective deployment of RPAS technologies in urban logistics systems, addressing key challenges of modern supply chains and supporting future sustainable transportation initiatives.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".