An Assessment of the Use of Autonomous Ground Vehicles for Last-mile Parcel Delivery
Bibliographic record
Abstract
Last-mile parcel delivery is a particularly costly element of the freight supply chain. The high cost of last-mile delivery can be attributed to the complexities associated with business to consumer e-commerce and current labour-intensive delivery methods. This thesis quanties the cost savings associated with implementing an automated last-mile delivery system. A literature review focused on vehicle routing problems and their applications to automated delivery systems is presented. Parcel demand data are provided by a large courier company operating in Canada. These data are described with gures and summary statistics. A novel synchronized split-delivery vehicle routing problem is formulated, which ensures delivery vehicles arrive at their destinations at the same time as all others if deliveries are split between vehicles. The model is applied to the sample data to compare cost of operating an automated system with the current manual system. Finally, recommendations to the data provider on implementing such a system are made.
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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 | 0.000 | 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 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".