An innovative framework integrating MILP and a parallel optimal algorithm for UAV-Enabled last-Mile delivery
Why this work is in the frame
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Bibliographic record
Abstract
Urban last-mile delivery faces scalability, cost, and environmental challenges due to truck-based systems’ congestion and emissions. This study proposes a Customer-Centric UAV Last-Mile Delivery (CULMD) framework, eliminating truck dependency by optimising UAV routing, charging infrastructure, and sequencing for sustainable urban logistics. We introduce the Parallel Optimal Algorithm with MILP (POAM), a novel approach that decomposes the problem into two sub-problems: parallelised exact combinatorial optimisation for tour and parcel allocation, and MILP-based routing. POAM leverages multi-core CPU parallelisation to solve tour allocation across multiple regions and delivery windows concurrently, ensuring global optimality while reducing runtime by 21.5% compared to the Two-Stage Model (TSM) and 16-fold compared to the Integrated Model (IM). It outperforms metaheuristics like the Artificial Lemming Algorithm (ALA) and Hybrid Genetic Algorithm with Type-Aware Chromosomes (HGATAC+) by 12% and 11% in objective value, respectively. Sensitivity analyses show a 20% increase in regions cuts runtime by 68%, and a 20% increase in UAV load capacity reduces it by 22%. The CULMD framework, powered by POAM, advances sustainable logistics by minimising costs and environmental impacts, offering scalable solutions for urban delivery systems.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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