Improved Genetic Algorithm For Optimization Of Cargo Space In Stereo Warehouse

With the rapid development of intelligent manufacturing in the industrial field,warehousing intelligence has also gradually emerged.In the warehousing link,the automated three-dimensional warehouse as the main body,its application is particularly wide.Due to the expansion of enterprise production scale,the traditional goods storage strategy no longer meets the demand,if not prior configuration of the automated three-dimensional warehouse space,will cause the consequences of warehouse access operations blockage or even interruption.Therefore,this paper is aimed at single-aisle and multi-aisle two modes of automated three-dimensional warehouse configuration of the cargo space problem for research,the main work is as follows:(1)The composition,types and operational processes of the storage system are sorted out,the strategy and allocation principles of cargo level optimization are analyzed,and the current status of domestic and international research on cargo level optimization is summarized and the shortcomings of existing research are identified.The theoretical knowledge of the automated warehouse,the object of this paper,is introduced in detail to lay the foundation for the study of cargo level optimization.(2)Aiming at the optimization problem of cargo level in single-aisle double-access platform automated three-dimensional warehouse,the mathematical model of cargo level allocation is established with the objectives of improving the handling efficiency of stacker cranes,improving the stability of shelves and similar quality of goods in the same aisle,designing the optimization model of genetic algorithm based on adaptive reversal method,and designing the adaptive reversal operator on the basis of cosine adaption to improve the problem that the genetic algorithm tends to fall into local convergence.The case verification shows that the establishment of the mathematical model is effective,the search results of the improved algorithm are faster and more accurate,and the obtained optimization scheme is more reasonable.(3)For the optimization of the cargo space of multi-aisle automated three-dimensional warehouse of auto parts enterprises,the optimization mathematical model is established with the optimization objectives of reducing handling time,lowering the center of gravity of the shelf and related product space aggregation,and the improved genetic algorithm is designed to optimize the model,and the algorithm adopts the adaptive cross-variance operator based on Sigmoid function on the basis of various group evolution strategies to further expand the algorithm search The algorithm uses adaptive cross-variance operator based on Sigmoid function to further expand the search area and improve the convergence accuracy.The experimental results show that the improved genetic algorithm outperforms the genetic algorithm and the multiple swarm genetic algorithm in terms of convergence speed and search accuracy,and the algorithm is more accurate and stable in terms of performance when dealing with different sizes of goods.(4)Combining the simulation elements and technical requirements of the digital factory,the virtual simulation experiment is designed with the whole life cycle process of factory production-transportation-distribution.Using Siemens PlantSimulation software for simulation,we observe the operation status of each link of the simulation process in a three-dimensional framework,verify the feasibility of the scheme from a dynamic perspective,realize the automation and flexibility of manufacturing and warehousing,and meet the relevant requirements of digital manufacturing.