Optimal Placement of Drone Delivery Stations and Demand Allocation using Bio-inspired Algorithms
Bibliographic record
Abstract
The last-mile delivery problem has recently received more attention due to dramatic increase in e-commerce and one-day delivery options. One delivery method has been in the forefront of last mile delivery research: drone delivery. Particularly, hybrid truck-drone delivery systems which attempt to overcome the limitations of only using drones. Researchers have attempted to improve drone routing and scheduling, but not many have studied the required infrastructure, including drone stations for recharging and pick-up of packages. This paper tackles the placement of drone delivery stations using bio-inspired optimization algorithms. The solution framework consists of two stages. The first stage tackles the location planning problem of stations, while the second stage deals with the allocation of delivery demand to located stations. The conventional k-means algorithm is used as a baseline for the location planning problem, while the greedy algorithm is used as a baseline for the demand allocation problem. The results shows that the simulated annealing algorithm achieves a 14% reduction in total cost and 6.2× run time improvement, whereas the genetic algorithm achieves a reduction of 10% in total cost and 1.8× run time improvement.
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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".