Research On The Optimization Problem Of Mobile Rack Storage Space Allocation Based On AGV

With the rapid development of e-commerce,the degree of automation in warehouse centers get higher and higher.Aiming at the situation where the storage volume and variety of fast-moving goods in general large-scale warehouse centers are large,the use of AGV(Automated Guided Vehicle)trolleys the "person-to-person" picking operation model emerged at the historic moment and has been gradually adopted by many e-commerce storage centers.The use of AGV to carry mobile three-dimensional racks replaces the movement of staff.One rack can even be picked at the same time for multiple SKU,which improve the efficiency of order picking and reduce the cost of human resources.With the diversified changes in customer order requirements,the traditional single random storage location and classified storage location strategy can no longer meet the needs of storage location planning,which affects the efficiency of warehouse operations.Therefore,rationally optimizing the allocation of storage locations and improving the operation efficiency of AGV trolleys is one of the core operations of the logistics storage center.This article introduces the research background and significance of storage allocation and relevant literatures on storage allocation optimization,and then confirms the research content and research methods of this article.Specific research is carried out from two perspectives of optimized storage allocation model and algorithm solution.Taking the AGV handling mobile shelves in the warehouse as the research background,the SKU turnover principle and the SKU correlation principle are used to allocate the storage space of the goods.Store high turnover rate shelves closer to the picking port,and store low turnover rate shelves far away from the picking port;store SKUs with high relevance and high frequency of ordering as close as possible to the same place On the shelf.Finally,a multi-objective storage allocation optimization model was constructed with the shortest SKU entry and exit distance,the highest overall stability of the mobile shelf,and the shortest distance between related SKUs.When solving the storage allocation optimization model,first,according to the warehouse’s historical orders,the FP-Growth algorithm is used to calculate the correlation and confidence between SKUs,and the SKUs that frequently appear in the same order are stored on the same shelf.Secondly,an improved multi-population genetic algorithm is used to solve the multi-objective storage allocation optimization model.Finally,in the case analysis part,the Y company storage center is selected as the case study and analysis object,and the relevant data is obtained in combination with the actual research situation.Genetic algorithm(GA),multi-group genetic algorithm(MPGA)and improved multi-group genetic algorithm(IMPGA)are used to solve the optimal model of storage allocation in this paper one by one.The calculation results show that when the IMPGA algorithm is used to solve the storage location optimization model,the optimization effect of the IMPGA algorithm is more significant in terms of the SKU entry and exit distance,the overall shelf stability,and the distance between related SKUs.At the same time,IMPGA algorithm avoids the premature convergence of traditional GA algorithm when solving problems.Therefore,on the basis of using the FP-Growth algorithm to mine frequent it emsets,the IMPGA algorithm is used to optimize the storage location,which provides a new idea for the storage location optimization problem of modern storage centers.