Research On Dynamic Picking Optimization Of Distribution Center

With the rapid development of E-commerce,the requirements of the distribution center for picking operations continue to increase.And the level of picking efficiency play a key role in the work efficiency of the entire distribution center.In order to improve the efficiency of the entire distribution center,the massive data of the picking operation was analyzed by data mining technology.At the same time,the mining useful information was feed back to the operation,thereby optimizing the picking operation and reducing the picking time.This paper optimizes the picking operation by means of storage allocation,order batching and picking path.Firstly,based on a large amount of historical order data and warehouse layout and storage strategy,the EIQ-ABC method was used to initially assign storage locations in the warehouse.Meanwhile,a data mining association algorithm was used to comprehensively analyze the order frequency and correlation of goods.In order to rationalize the results of storage allocation,related goods were arranged in adjacent storage.Second,the order of batching and picking paths was optimized.The joint optimization model with the shortest total picking time on the premise of a large number of orders was established.Considering the complexity of double optimization,a nested genetic algorithm was designed to solve the model.When the capacity of the picking truck was satisfied,the clustering algorithm was used to obtain the initial order batching results as the initial solution of the outer optimization.The result of order batching was optimized by the outer genetic algorithm.And the picking path was optimized by the inner genetic algorithm.The optimal value was obtained by interrelation and mutual feedback between the inner and outer layers.The results show that,compared with the single-selection and batch-by-step optimization strategies,the joint optimization model yields the least selection time.Moreover,as the order volume increases,the overall picking distance and time further decrease.It solves the problem that the traditional order batching and the stepwise optimization of the picking path lead to the difficulty of obtaining the overall optimal solution.Thirdly,on the basis of the simultaneous picking of a large number of picking orders,a dynamic picking method was designed for collaborative work of position adjustment and picking.The mathematical model was built with the shortest picking time as the objective function.The optimal solution was obtained by designing genetic algorithm and hybrid genetic simulated annealing algorithm.The results show that,compared with the genetic algorithm,the hybrid genetic algorithm is more suitable for solving the model,the algorithm performance is more stable,and the picking time and picking distance obtained by the solution are also better.Under the conditions of different picking orders,compared with the static picking strategy,dynamically adjusting the position of the goods in the subsequent picking orders can further reduce the picking time and increase the average picking efficiency by 5.38%.At the same time,as the quantity of picking orders increases,the more the picking time and the picking distance are saved.Finally,on the basis of the dynamic picking strategy,the ordering of picking orders,position adjustment and route optimization are integrated.The improved dynamic picking optimization method was designed,and a mathematical model aimed at the shortest picking time was established.And the model is solved by genetic and hybrid genetic algorithms.The results show that under different picking order conditions,the time and distance for improving the dynamic picking strategy are smaller than the dynamic picking strategy,and the average picking efficiency is increased by 15.44%.It can be seen that the strategy of improving dynamic picking is better,and as the amount of picking orders increases,the time saving percentage of improving the dynamic picking strategy is larger.It shows that this strategy is more suitable for solving the task of picking large quantities of orders.As a result,order picking efficiency is improved,and operating costs are reduced on a large scale.The conclusions of the model and numerical simulation in this paper provide a theoretical reference for the optimization of picking in the distribution center.