Automated Logistics Control Model Based on Improved Ant Colony Algorithm

Abstract

With the rapid development of modern logistics industry, traditional automated logistics control systems often lack transparency and visualization of the entire supply chain. It cannot comprehensively manage and optimize the entire supply chain. Therefore, an automated logistics control model is constructed based on ant colony algorithm and logistic chaotic mapping. By simulating the pheromone transmission process of ants, the optimal logistics transportation path is found. From the experimental results, the improved Ant Colony Algorithm (ACA) was tested on the DT100dataset, achieving an optimal solution within 200 iterations. Compared with traditional methods, the cost was reduced by 0.25 units. The distance solution image of the improved ACA was overall concave downwards, with a significant decrease after 20 iterations. The designed automated logistics control system used map APIs and sensor data to obtain real-time delivery routes. On average, each logistics node consumed 1.01% of electricity. Compared with traditional methods, it had the highest prediction accuracy, with an R2 of 0.98. In summary, the improved ant colony algorithm can optimize logistics delivery paths, reduce delivery time and cost, improve delivery efficiency, and reduce delivery energy consumption.