A dynamic drone routing problem with uncertain demand and energy consumption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work addresses a drone routing problem with an identical fleet performing same-day deliveries in a dynamic and uncertain environment. We model the problem as a Markov Decision Process to capture the stochastic nature of customer demand and the uncertainty in energy consumption due to varying payloads and weather conditions. To tackle this problem, we propose an approximate dynamic algorithm that integrates routing planning, drone usage, and battery management. Uncertainty in energy consumption is dealt with the chance constraints ensuring that drone trips are completed safely, preventing premature returns to the depot. The proposed approach features a cost function approximation policy that accounts for a restricted number of trips to be assigned to drones. This ensures that the drones are ready at the depot to fulfill new requests that may arise during the day. Extensive computational experiments on 300 instances validate the effectiveness of our method, demonstrating its superiority over a myopic strategy, a policy function approximation approach, and an oracle method, thus highlighting its potential for practical applications.
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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 | 0.001 | 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 it