Optimization scheme for urban logistics emission reduction based on improved ACO algorithm and time window constraints

Urban logistics is facing the dilemma of concentrated carbon emissions caused by high-density transportation networks and inefficient end of pipe distribution exacerbating pollution. With the popularization of electric vehicles and the promotion of green supply chain policies, emission reduction needs to shift toward systematic optimization. This study aims to achieve systematic emission reduction in infrastructure layout and transportation efficiency through collaborative optimization of logistics distribution center location and route. It divides the distribution area through the “elbow method–K-means clustering”, combines the center of gravity method to determine the location of the distribution center, and then constructs a "total cost minimization" model covering fixed, transportation, punishment, cargo damage, and carbon emission costs. This model is solved using an I-ACO that combines variable neighborhood search and dynamic pheromone updating. The results showed that the I-ACO reduced the total cost by 10.2% and the carbon emission cost by 17.3% compared to the traditional ACO. When the number of clusters was 4, the total cost of the improved site selection method was 14,800 yuan, which was 13.95% lower than the traditional random clustering method. When the demand concentration was 60%, the total carbon emissions of the improved plan were reduced by 25% compared to the traditional plan. The research model has validated the effectiveness of the collaborative optimization mechanism and algorithm improvement between site selection and path selection, breaking the limitations of traditional research that focuses solely on optimization and lacks cost dimensions. This study provides an integrated optimization solution for urban logistics that combines economy and low-carbon, helping to efficiently implement green supply chain policies.