Optimization of low-carbon multi-temperature joint distribution for fresh agricultural products under 3D loading constraints

With the growing demand for fresh agricultural products, improving the efficiency and sustainability of cold-chain distribution has become increasingly important. Multi-temperature joint distribution provides an effective solution for serving products with different temperature requirements, yet its implementation remains challenging due to the need to coordinate vehicle routing, three-dimensional loading, and carbon-emission reduction objectives. To address this issue, this paper develops a low-carbon multi-temperature joint distribution optimization model under three-dimensional loading constraints. A hybrid algorithm integrating genetic algorithm and tabu search is proposed to solve the model efficiently. The proposed approach is validated using real-world data collected from a fresh agricultural products supply chain company. The results show that the multi-temperature joint distribution mode reduces total operating costs by 30.04% and carbon emissions by 30.62% compared with the conventional single-temperature distribution mode. Moreover, the proposed hybrid algorithm achieves faster convergence and better solution quality than the conventional genetic algorithm. These findings demonstrate the effectiveness of integrating three-dimensional loading, multi-temperature distribution, and low-carbon objectives within a unified optimization framework, providing practical support for distribution planning and decision-making in cold-chain logistics.