An Optimization of Cold-chain Logistics Routing for Cost and Carbon Reduction

The prevalence of low carbon and environmental protection philosophy and the increasing importance of sustainable supply chain development have rendered cold-chain logistics increasingly significant. This study addresses the optimal route planning challenge from warehouses to distribution centers, focusing on minimizing costs and carbon emissions in cold-chain logistics. A novel vehicle route planning model for cold-chain logistics was developed, targeting cost savings and reducing carbon emissions. Genetic algorithm and ant colony optimization were employed to determine the optimal route, demonstrating effective performance in solving the optimization problem. The study achieved a more optimal distribution path by combining the rapid convergence of ant colony optimization with the computational efficiency of genetic algorithm. Experimental results validate the enhanced algorithm’s ability to swiftly obtain an optimal cold-chain logistics distribution scheme at a lower cost. This research contributes to cost reduction in distribution and significantly lowers carbon emissions, thereby yielding dual economic and social benefits.