MCAH-ACO: A Multi-Criteria Adaptive Hybrid Ant Colony Optimization for Last-Mile Delivery Vehicle Routing
Why this work is in the frame
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Bibliographic record
Abstract
Last-mile delivery routing has become a pressing challenge as e-commerce volumes continue to surge. Most existing vehicle routing models focus on minimizing just one criterion---travel distance or time---while overlooking social and environmental costs. How can we balance these competing factors? This paper present MCAH-ACO, a Multi-Criteria Adaptive Hybrid Ant Colony Optimization algorithm that treats delivery routing as a Multiple Traveling Salesman Problem (MTSP). Our approach is distinguished by three mechanisms. First, multi-criteria pheromone decomposition maintain separate pheromone matrices for each objective. Second, an adaptive weight balancing scheme adjust criterion weights on the fly, preventing any single factor from dominating. Third, 2-opt local search works alongside an elite archive that preserves solution diversity. The cost function capture four aspects: distance, time, social-environmental impact, and safety. We tested MCAH-ACO on real delivery data from the Greater Toronto Area. Results show 12.3% lower total cost and 18.7% fewer safety-critical events versus the strongest baseline (Max--Min Ant System), with runtime remaining competitive.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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