The optimization of automated goods dynamic allocation and warehousing model

Abstract

In the development of modern logistics, the role of automated cargo warehousing is gradually reflected, which is essential for the automatic distribution of goods. This paper briefly introduced the automatic location allocation model and the particle swarm optimization (PSO) algorithm used to optimize the model. At the same time, it introduced the concept of genetic operator and multi-group co-evolution to improve the algorithm, and then the simulation analysis of standard PSO and improved PSO was performed on MATLAB software. The results showed that the improved PSO iterated fewer times and get better solution sets; compared with the manual allocation scheme, the improved PSO calculation reduced more warehousing time, lowered more center of gravity height, and improved shelf stability. In summary, the improved PSO algorithm can effectively optimize the automated goods dynamic allocation and warehousing model.

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

“…Storage space optimization involves multiple management decision-making objectives. A review of the literature by various scholars [54,55] and the consideration of the requirements of business operations helped identify the objective functions of optimization as minimizing resource loss, maximizing goods accumulation, and minimizing goods selection time. ey aimed to realize the rational use of resources and improve overall business performance.…”