Design of supply chain management optimization model based on improved genetic algorithm

Abstract In the globalized and fiercely competitive market environment, supply chain management plays a pivotal role in the operation and development of enterprises. A supply chain management optimization model based on improved genetic algorithm is proposed. Through simulation experiments, the performance in time, cost, and efficiency is verified. It is also compared with traditional strategies. The experimental results indicate that most of the time changes in the simulation process and standard delivery are roughly the same. The subtle difference appears after 600 s. There is a significant difference at 1,100 s, but it is quickly self calibrate. The inventory change value based on the inventory turnover control strategy is 0.2547, while the inventory change value based on the improved genetic algorithm control strategy is 0.2734, with a difference of 5.9 %. The majority of supply chain efficiency under two control strategies is located in high efficiency areas, with efficiency greater than 92 %. When delivering, in addition to supply efficiency, the efficiency of turnover days also needs to be considered. Most of the loss rate points are within 220 g/km. Compared to traditional control strategies, control strategies based on improved genetic algorithms have some advantages in the distribution of loss rates, with more points distributed between 190 and 210 g/km. Furthermore, compared with traditional methods, the proposed model has advantages in cost, time, and efficiency. Therefore, this research provides an optimization strategy for supply chain management, which is beneficial for improving the operational efficiency and development potential of enterprises in a globalized and fiercely competitive market environment.