A Multi-Objective Low-Carbon Routing Model for Perishable Agri-Food Supply Chains: An Improved Ant Colony Optimization Approach with Dynamic Risk-Aware Local Search

• Develops a multi-objective routing model integrating cost, carbon emission, spoilage, and customer satisfaction for perishable agri-food supply chains. • Proposes an improved ant colony algorithm with dynamic pheromone updates and risk-aware 2-opt local search to enhance convergence and avoid local optima. • Validates the model using a real-world case in China, demonstrating a 19% reduction in carbon emissions and 46% customer satisfaction. • Achieves synergistic optimization of route length, product freshness, and environmental impact, supporting sustainable cold-chain logistics. • Provides an engineering-oriented decision-support tool adaptable to dynamic and constrained transportation scenarios. This study proposes a multi-objective optimization model for designing low-carbon transportation routes for perishable agricultural products, integrating environmental sustainability, economic efficiency, and customer satisfaction. To address the limitations of conventional ant colony algorithms—such as local optima entrapment and slow convergence—we introduce an improved ant colony algorithm (IACO) enhanced with a dynamic pheromone update mechanism and a risk-aware 2-opt local search strategy. The model simultaneously minimizes total cost, carbon emissions, and product loss while maximizing customer satisfaction, incorporating realistic constraints such as vehicle capacity, time windows, and cold-chain temperature–humidity requirements. A simulation-based case study of a Chinese agricultural wholesale center serving 20 supermarkets demonstrates that the proposed IACO reduces carbon emissions by 19% (to 73.08 kg) and improves customer satisfaction to 64%, outperforming particle swarm optimization (31%) and simulated annealing (32%). Moreover, 80% of routes are confined within 100 km, achieving a synergistic balance of short routes, low spoilage, and low emissions. The research provides a computationally efficient and scenario-adaptive framework for green cold-chain logistics, contributing to sustainable agri-food supply chain engineering.