Construction and Practice of Intelligent Warehouse Management Decision Support System Based on Artificial Intelligence

With the rapid development of globalization and information technology, warehouse management(WM), as a key link in the logistics supply chain, has become increasingly important. However, the traditional manual storage management can not meet the high requirements of efficiency, cost and accuracy of modern enterprises. Therefore, the intelligent WM decision system of artificial intelligence(AI) has become the product of the development of The Times. Warehousing cost accounts for nearly one-third of the total logistics cost of an enterprise, and its management efficiency directly affects the response speed of the enterprise’s supply chain and customer satisfaction. With the help of AI technology, the realization of intelligent and automated WM has become the key to enhance the competitiveness of enterprises. In this paper, the experimental method and comparison method are used to carry out systematic tests on modules such as incoming management, outgoing management, inventory management and report analysis, and the results of system reaction time, concurrency, accuracy, security, reliability, scalability, recall rate and efficiency are compared. The effects of genetic algorithm, simulated annealing and ant colony algorithm on the improvement of WM efficiency and cost reduction are also analyzed. The experimental results show that the genetic algorithm reduces the cost of the WM decision system by 23%. Simulated annealing reduced costs by 15% and ant colony algorithm by 10%. Therefore, the AI algorithm provides many support on the system.