Time and Cost Efficiency of Autonomous Vehicles in the Last-Mile Delivery: A UK Case
Bibliographic record
Abstract
In recent years, last-mile delivery has become an increasingly important area in the global supply chain. Practically, there has been an increasing worldwide interest in developing the last-mile delivery robots/vehicles to increase the efficiency of the whole supply chain. Theoretically, several researchers have suggested that using autonomous robots brings more efficiency for delivery. However, almost no current studies consider a specific last-mile delivery activity – transport from supermarkets after loading to pick-up stations before unloading. The goal of this study is to investigate whether and how autonomous vehicles/robots can address the issue of cost and time efficiency. Specifically, this research aims at identifying the time and cost structures of using autonomous vehicles for the delivery along the chosen route – a single way from the Sainsbury’s supermarket to the Amazon pick-up station at Coventry. Furthermore, the research aims to find whether using autonomous vehicles is more efficient in time and/or cost than using vans with drivers for this route.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".