Transportation Route Optimisation Method for Agricultural Product Logistics Based on TSVNS

As the demand for agricultural product logistics grows rapidly and requirements for timeliness and freshness in cold chain transportation increase, existing vehicle route optimisation methods face challenges in addressing multi-objective conflicts and dynamic environmental changes. To address these issues, the study proposes a hybrid optimisation algorithm based on tabu search and variable neighbourhood search, combined with a logistics route method incorporating non-dominated sorting genetic algorithm, and designs a transportation route model for agricultural product logistics vehicles suitable for cold chain transportation. Experimental results of logistics route planning show that the proposed model generates 8 paths, significantly fewer than the comparison models. A real-world scenario validation under different temperature conditions shows that as the temperature difference increases from 5°C to 45°C, the cargo loss increases from 365.32 yuan to 552.93 yuan, and the refrigeration cost also gradually increases. Other indicators similarly rise with the increasing temperature difference, and the total distribution cost reaches 7,069.73 RMB. The experimental results indicate that the proposed route optimisation model can be applied to agricultural product logistics scenarios with complex constraints and is of significant importance for improving cold chain transportation efficiency, reducing logistics costs and ensuring agricultural product quality.