Planning for optimised local delivery using sidewalk robots and mothership vans
Bibliographic record
Abstract
Sidewalk Autonomous Delivery Robots (SADRs) are vehicles that utilise pedestrian and cycle pathways to carry goods over the last mile. To overcome the typically short-range of SADRs, Mothership vans (MSs) that can carry SADRs from logistics centres to service areas have been proposed. In this paper, we develop analytical models to evaluate two routing strategies of MS and SADRs to service non-urgent, non-medical package delivery operations. The strategies introduced are: the ‘Series’ strategy, where each SADR is dropped off by the MS to service their own sub-region while being resupplied by repeated tours of the MS; and the ‘Parallel’ strategy, where SADRs are deployed simultaneously in one sub-region and the MS waits for their return before moving to the next deployment location. We undertake a cost and travel demand comparison between the MS strategies and conventional truck delivery. To minimise sidewalk travel, the ‘Series’ strategy should be used, rather than the ‘Parallel’ strategy, when there are more SADRs per MS than reloads per MS, and when there is only one SADR per MS. We determine closed-form solutions for the mean demand density range where an MS strategy may be both feasible and profitable, for a given SADR battery capacity and when compared to conventional truck delivery. Finally, we show in a numerical example that the optimal design of MS and SADRs is sensitive to the service area parameters of logistics sprawl and mean demand density, and relatively less sensitive to the unit moving and capital costs of the vehicles.
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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 | 0.001 | 0.001 |
| 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".