The Construction of Intelligent Supply Chain System for Agricultural Products Based on Improved Ant Colony Algorithm

Abstract

With the rapid development of China itself, the supply chain system has become an effective tool to enhance economic competitiveness, and the intelligent supply chain system integrates innovative technologies such as the Internet, the Internet of Things, cloud computing, and big data into the industrial supply chain management. The ant colony algorithm shows super flexibility and robustness at the level of optimizing many combination problems; the intelligent supply chain architecture of agricultural products is essentially the rational allocation of resources, which is a dynamic combination of resources and tasks that combines a variety of combinations to select the most appropriate and optimal performance. In this paper, the architecture of the intelligent supply chain of agricultural products is studied in combination with the improved ant colony algorithm, and some improvement strategies are adopted to avoid falling into local optimization or local convergence in order to avoid falling into local optimization or local convergence. The improvements of the algorithm mainly include the following: improving the path search of individual ants, improving the pheromone update strategy, improving the selection probability, and improving the dynamic growth mechanism of ants. In this paper, the VRPTW mathematical model, the dataset TSPLIB, and the operation data of the intelligent supply chain of agricultural products are used for test verification; the results can be obtained by verifying the results of the experiment closer to the optimal solution, and the efficiency of the algorithm is significantly improved. Therefore, through the improvement and optimization of the ant colony algorithm, it is very suitable for solving and optimizing the intelligent supply chain system of agricultural products.