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

## TL;DR

This paper introduces a new algorithm for optimizing delivery routes that considers multiple factors like distance, time, and environmental impact, showing better performance than existing methods.

## Contribution

The novel MCAH-ACO algorithm introduces multi-criteria pheromone decomposition, adaptive weight balancing, and 2-opt local search for vehicle routing.

## Key findings

- MCAH-ACO achieved 12.3% lower total cost compared to the best baseline methods.
- The algorithm reduced safety-critical events by 18.7% while maintaining computational efficiency.
- Experiments used real-world data from the Greater Toronto Area.

## Abstract

The growing demand for efficient last-mile delivery has made routing optimization a critical challenge for logistics providers. Traditional vehicle routing models typically minimize a single criterion, such as travel distance or time, without considering broader social and environmental impacts. This paper proposes a novel Multi-Criteria Adaptive Hybrid Ant Colony Optimization (MCAH-ACO) algorithm for solving the delivery vehicle routing problem formulated as a Multiple Traveling Salesman Problem (MTSP). The proposed MCAH-ACO introduces three key innovations: a multi-criteria pheromone decomposition strategy that maintains separate pheromone matrices for each optimization objective, an adaptive weight balancing mechanism that dynamically adjusts criterion weights to prevent dominance by any single objective, and a 2-opt local search enhancement integrated with elite archive diversity preservation. A comprehensive cost function is designed to integrate four categories of factors: distance, time, social-environmental impact, and safety. Extensive experiments on real-world data from the Greater Toronto Area demonstrate that MCAH-ACO significantly outperforms existing approaches including Genetic Algorithm (GA), Adaptive GA, and standard Max–Min Ant System (MMAS), achieving 12.3% lower total cost and 18.7% fewer safety-critical events compared with the best baseline while maintaining computational efficiency.