Optimization of Cold Chain Logistics Distribution Using ACO Algorithms

To address the high energy consumption and emissions of Cold Chain Logistics (CCL), this study proposes a distribution path optimization model based on an improved ant colony optimization algorithm. The model considers transportation, vehicle usage, energy, and carbon emission costs to achieve a balance between cost, carbon reduction, and distribution efficiency. Results indicated that the enhanced algorithm reduces the mean square error and computation time by 48.39% and 14.73%, respectively. The optimal path was found to lower cargo damage by 31.82% and improve customer satisfaction by 7.52%. Additionally, the model decreases the transportation, vehicle, energy, and carbon emission costs by 14.53%, 12.99%, 13.85%, and 17.41%, respectively. The results demonstrate that the proposed CCL distribution optimization model effectively balances route connectivity, low-carbon objectives, and customer satisfaction. It provides quantitative evidence to help governments establish green logistics subsidy standards and enables enterprises to fulfill their carbon reduction responsibilities. These findings are of practical significance for advancing policy implementation and promoting sustainable development within the CCL industry