MCAH-ACO: A Multi-Criteria Adaptive Hybrid Ant Colony Optimization for Last-Mile Delivery Vehicle Routing
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".