Analysis of the material distribution system of wise information technology of 120 under deep learning

Abstract

In order to explore the feasibility of using deep learning in the Wise Information Technology of 120 (WIT120) material distribution system under deep learning, in the study, first, the Hopfield neural network and simulated annealing (SA) algorithm are used to study the path optimization in the WIT120 material distribution system. Aiming at the poor optimization and robustness of the traditional Hopfield neural network and the low optimization efficiency of SA algorithm, in this study, a memory device is added to the SA algorithm, the distribution starting points of the two algorithms are fixed, and an improved hybrid algorithm is designed by combining Hopfield neural network and SA algorithm. Then, the improved hybrid algorithm of the traditional Hopfield neural network and SA algorithm is used in the actual medical material distribution system, and comparative analysis of the distribution route, cost, and scheduling time before and after using the proposed algorithm is performed. Besides, the difference between the proposed algorithm and other algorithms in path optimization and path planning efficiency is compared and analyzed. The results show that the improved hybrid algorithm proposed in this research reduces the total mileage of distribution by 46.9%, costs by 33.9%, and the time required for scheduling by 99% after applied in the medical material distribution system. It is also better than traditional analog algorithms and neural networks in distribution path optimization. The path optimization results show that the shortest path produced by the improved hybrid algorithm in this study is 27.85, which is superior to the traditional SA algorithm, Hopfield neural network, and genetic algorithm (the shortest paths obtained are 41.21, 44.63 and 36.48, respectively). Compared with other optimization algorithms, the path planning efficiency of the hybrid algorithm is the highest (the amount of distribution tasks completed in unit time is 22). In conclusion, the improved hybrid algorithm proposed in this study can effectively reduce distribution costs, shorten transportation routes, and improve optimization efficiency, indicating that the hybrid algorithm proposed in this study is effective and practical when applied in the medical material distribution system, which can provide reference value for medical material distribution and provide data support for the management of medical material distribution.