Balancing Trade-Offs: The Energy Efficiency of Unmanned Aircraft Systems Integration in Last-Mile Delivery and Operational Policy Restrictions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it