Simulation Study On Goods Picking Strategy With Randomly Arriving Orders

With the rapid development of e-commerce and the ever-changing customer demands,higher requirements have been put forward for the picking efficiency of warehouse and distribution centers.As key factors affecting picking efficiency,location assignment and order batching have become important issues that companies urgently need to solve.Based on the goods-to-person picking mode,this paper studies the problem of location assignment and order batching for randomly arriving orders.Firstly,a dynamic location assignment and order batching model is constructed based on the consideration of dynamic changes in commodity demand frequency and correlation over time.Secondly,new picking strategies and solution algorithms are designed to address the shortcomings of existing picking strategies.Finally,simulation experiments are conducted to compare the experimental results of different picking strategies and verify the effectiveness of the dynamic picking strategy proposed in this paper.The main research content and achievements of this paper are as follows:Construction of dynamic location assignment and order batching model and algorithm design.Firstly,the picking workflow of warehouse and distribution centers under the goods-to-person picking mode is analyzed,and the factors affecting the picking efficiency that currently exist in order picking are identified.Secondly,taking into account the dynamic changes in commodity demand frequency and correlation over different time periods,a dynamic location assignment and order batching model is established.Finally,aiming to minimize the transportation distance of goods during the picking process,new location assignment and order batching strategies and solution algorithms are designed to deal with the problems of large fluctuations in customer demand and high timeliness requirements in practical situations.Simulation experiments and case analysis of dynamic location assignment and order batching.Firstly,based on the simulation method of discrete-event systems and multiple intelligent agents,an order picking model for randomly arriving orders in warehouse and distribution centers is established,which simulates the location assignment and order batching process under the condition of randomly arriving orders and is divided into four parts:simulation of order data generation,initial location assignment,dynamic adjustment,and order batching process.Secondly,through experimental result data such as average order completion time,order waiting time,and unit time output,the existing picking strategy and the strategy proposed in this paper are compared,and the optimization effect of the location assignment and order batching proposed in this paper is analyzed.Finally,the impact of changes in order size and resource parameters on picking efficiency is analyzed,and the dynamic location assignment and order batching application strategies considering commodity correlation under different environments are explored.The study found that the location assignment and order batching picking strategy proposed in this paper is superior to random assignment,static assignment considering correlation,fixed time window,and variable time window batching strategies,and can significantly reduce order waiting time and AGV picking distance during the picking process while improving unit time output.