MCAH-ACO: A Multi-Criteria Adaptive Hybrid Ant Colony Optimization for Last-Mile Delivery Vehicle Routing

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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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