Balancing Trade-Offs: The Energy Efficiency of Unmanned Aircraft Systems Integration in Last-Mile Delivery and Operational Policy Restrictions
Bibliographic record
Abstract
Compared to the conventional transportation method of diesel cargo trucks, Unmanned Aircraft Systems (UAS) can potentially transform the logistics sector in the coming decade through their integration into last-mile delivery systems. Prior studies have demonstrated that UAS, given their versatility, can improve the overall energy efficiency of the system when used in conjunction with conventional delivery methods, thus offering a more environmentally sustainable approach to last-mile delivery. However, numerous challenges must be addressed for this integration to be feasible, with UAS regulations being a large limiting factor. UAS regulations are continuously evolving and oversee critical issues such as safety and privacy, but they impose many restrictions on UAS usage. This study aims to investigate this problem by examining the impacts of regulations on a heterogeneous UAS-integrated last-mile delivery. Specifically, we focus on a sustainability perspective and evaluate the effects of operational regulations on the energy efficiency of the delivery system. We develop an Ant Colony Optimization (ACO) system to simulate the truck-drone Heterogeneous Delivery Problem (HDP) with dynamic switch points. Results show that under current FAA regulations, a truck-drone hybrid delivery system saves approximately 1.60 US gallons of gasoline compared to a truck-only system. However, the energy consumption is competitive. Furthermore, we perform a sensitivity analysis to examine the effects of various flight parameters and no-fly zones on the energy consumption of the delivery operation. We find additional no-fly zones and the requirement to operate within the Visual Line-of-Sight (VLOS) of the operator to impact the allocation of the delivery system. This study serves as a reference point to guide the direction of future UAS policy-making and technological development, advocating for more sustainable forms of last-mile delivery and contributing to the realization of UAS technology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".