A vehicle routing problem with movement synchronization of drones, sidewalk robots, or foot-walkers
Bibliographic record
Abstract
The vehicle routing problem (VRP) and its variants have many city logistics applications, such as goods delivery. The VRP extension with movement synchronization (VRPMS) has potential applications of drone and robot technologies to assist with the delivery of parcels. VRPMS seeks the optimal route for a set of composite resources, e.g. delivery van with drones, or delivery van with sidewalk robots. This paper proposes an exact formulation of the problem, and a metaheuristic approach to solve larger instances of the VRPMS in order to assess the economic benefits of the different technologies. It is shown that with the current physical constraints of drone technology, assisted delivery with drones has some challenges because of its capacity. Sidewalk robots and walkers, however, do contribute a cost savings compared to truck deliveries. As the technology matures, the presented metaheuristic approach can be used to evaluate improved economic benefits and cost benefit ratios.
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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 | 0.002 |
| 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".