Collaborative Optimization Study Of Order,Goods Location And Path In Warehouse System

Storage,picking,and transportation are the three most important aspects in the operation of intelligent warehousing systems.Current research mainly focuses on storage location optimization,order picking optimization,and transportation paths optimization for these three aspects separately.However,in practical applications,each aspect is interdependent,and ignoring the impact of any one aspect cannot minimize the operating cost and achieve the highest efficiency of the entire system.Therefore,for improving the operational efficiency of intelligent warehousing systems,this thesis takes intelligent warehousing systems as the research object,with order structure factors as the link,studies the mutually coordinated strategy for inbound and outbound storage by collaboratively analyzing the influence of multiple factors such as order,location and path.A storage location optimization model based on the turnover rate of goods,the correlation between goods,and path is proposed in this thesis for the collaborative optimization problem of order-storage location.At the first place,the correlation between Stock Keeping Units(SKU)in historical orders is analyzed by the cosine similarity algorithm,dividing SKUs with high similarity into a whole,and calculating the turnover rate of SKUs.Then,the Genetic Algorithm Simulated Annealing(GASA)and the Improved Gray Wolf Optimizer(GWO)integrating Genetic Algorithm(GA)are proposed respectively in this thesis to solve the problem of storage location optimization,so as to obtain the optimal storage location of goods.Finally,the effectiveness of two improved algorithms in solving different dimensions of storage location optimization problems was verified through numerical examples.A picking strategy based on SKU clustering is proposed in this thesis for the collaborative optimization problem of order-path.This strategy focuses on the characteristics of goods placement after optimizing storage locations,and cooperatively optimizes the outbound process from two aspects: order allocation and path planning.Firstly,the K-Means clustering algorithm is used to analyze and cluster the SKUs in multiple real-time orders to determine the picking task of the multi load AGV,and the Ant Colony Optimization(ACO)is used to calculate the optimal picking order of the items in each picking task.Then,the ACO is improved,the AGV turning factor is introduced into the pheromone update rules,and the global search ability of ACO in the path planning problem is improved by adaptively updating the volatilization coefficient of pheromone.Finally,the simulation has verified that the picking strategy based on item clustering proposed in this thesis can effectively improve the efficiency of outbound operations.Based on the above research,resulting in a complete inbound and outbound process in this thesis where storage,picking,and transportation are are considered in collaboration.In addition,simulation experiments are conducted to compare the time to complete the order picking task before and after the collaborative optimization,and it is found that the collaborative optimization strategy of order,location and path proposed in this thesis can maximize the system operation efficiency,which verifies the applicability and effectiveness of the collaborative optimization strategy proposed in this thesis.This study provides a theoretical basis for the design of practical intelligent warehousing systems,and has important practical significance in improving the operational efficiency of the logistics industry and reducing operating costs.