The Ant Colony Optimization Algorithm is Used to Solve the Problem of Traditional Warehousing and Picking Path in Logistics Center

In order to solve the challenge of the traditional warehousing picking path problem in logistics center, in view of the shortcomings of the existing evolutionary algorithms, this study proposes an innovative traditional warehousing picking path problem method based on ant colony optimization algorithm. This new approach leverages the principles of biological evolution theory to precisely identify and locate key influencing factors, and accordingly makes a sensible classification of indicators to reduce possible interference. At the same time, using the unique mechanism of ant colony optimization algorithm, this scheme cleverly constructs the design strategy of traditional warehouse picking. The empirical results show that this scheme shows a significant improvement compisd with the traditional evolutionary algorithm in terms of key performance indicators such as the accuracy of the traditional warehousing picking path problem and the processing efficiency of key factors, showing its obvious strong advantages. In the logistics center, the traditional warehouse picking path problem plays a crucial role, which can accurately predict and optimize the growth trend and output results of the traditional warehouse picking path problem in the logistics center. However, when faced with complex simulation tasks, traditional evolutionary algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of the traditional warehousing and picking path problem optimized by ant colony optimization algorithm, and accurately controls the influencing parameters through biological evolution theory, and uses this as the road map for index allocation, and then uses ant colony optimization algorithm to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the problem of traditional warehousing and picking path in the logistics center, the simulation scheme based on ant colony optimization algorithm successfully overcomes the shortcomings of the traditional evolutionary algorithm, and significantly improves the accuracy and operation efficiency of the simulation.