A drone-assisted last-mile delivery framework for shipment prioritization in post-disaster and high demand periods
Bibliographic record
Abstract
The aftermath of a disaster can observe increased demands for online purchases and deliveries due to a decreased local supply. The increased demands may then induce delays in item delivery. Natural disasters may also result in epidemics and critical medical situations. Thus, the deliveries can include both life-essential items, such as medicine or medical equipment, and non-essential items, such as toys or decor products. Similar circumstances may be induced by other abnormal situations, such as pandemics or wars. It is then important to prioritize shipments to ensure early delivery of life-essential products at the expense of delaying non-essential items. In this work, we introduce a framework to prioritize shipments with application on drone-assisted last-mile deliveries. While the framework is general and can be used in different last-mile delivery types, the selection of drone-assisted deliveries is important for post-disaster situations to allow for contactless deliveries in case of epidemic outbreaks, and to reach destinations inaccessible by vehicles due to damaged roads and infrastructure. We consider different priority levels ranging from high priority to low priority items. High priority items must be delivered as soon as possible, medium priority items must be delivered within a certain time frame, and low priority items can be delivered after the high and medium priority conditions are met. We introduce a mixed integer program to model the drone-assisted last-mile deliveries with the prioritization scheme and propose a solution framework to systematically solve the problem. Finally, we illustrate the benefits of the model through numerical cases and simulations, and discuss its implications. • Drone-assisted last-mile deliveries in post-disaster and high-demand periods. • A prioritization scheme to ensure the prompt delivery of essential items. • A mixed integer program formulation is designed and presented. • A solution framework that aids in solving the problem is designed and presented. • Numerical cases and simulations are presented and discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".