Strategic Network Design for Parcel Delivery with Drones Under Competition
Bibliographic record
Abstract
This paper studies the economic desirability of unmanned aerial vehicle (UAV) parcel delivery and its effect on e-retailer distribution networks while taking into account technological limitations, government regulations, and customer behavior. We consider an e-retailer offering multiple same-day delivery services including a fast UAV service and develop a distribution network design formulation under service-based competition where the services offered by the e-retailer not only compete with the stores (convenience, grocery, etc.) but also with each other. Competition is incorporated using the multinomial logit market share model. To solve the resulting nonlinear mathematical formulation, we develop a novel logic-based Benders decomposition approach. We build a case based on New York City, carry out extensive numerical testing, and perform sensitivity analyses over delivery charge, delivery time, government regulations, technological limitations, customer behavior, and market size. The results show that government regulations, technological limitations, and service charge decisions each play a vital role in the future of UAV delivery.
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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".