(untitled) (Informatica, 2024)
In the context of 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. To this end, an automated logistics control model is constructed based on ant colony algorithm and combined with 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 could find the optimal solution with 200 iterations. The result value of the cost solution was 0.25 units lower than the average cost. Compared with the distance solutions of the other three algorithms, the distance solution image of the research algorithm was overall downward, with a significant decrease after 20 iterations. The power consumption of the research model was relatively low, with an average consumption of 1.01% for per logistics node. The prediction accuracy of the research model was the highest, with an R2 of 0.98. In summary, the improved ant colony algorithm can optimize logistics delivery paths, reduce delivery time and costs, improve delivery efficiency, and reduce delivery energy consumption.