The Research On Scheduling Optimization Algorithm Of Intelligent Warehousing System

As an important part of modern industry,logistics is the third source of profit for social and economic development.With the arrival of Industry 4.0,intelligence is the trend of the current logistics industry.As a key link in the logistics industry,the intelligent storage system affects the turnover rate of the entire logistics system.This paper studies the key issues of efficiency improvement in each scheduling part of intelligent warehousing system,improves the efficiency of warehousing operation,and develops a intelligent warehousing scheduling system.The paper mainly includes the following aspects:(1)Analyzed the key issues in the scheduling of intelligent warehouse system,and analyzed the importance and optimization of multi-subsystem joint scheduling,picking task scheduling and AGVs system task scheduling.(2)Aiming at the problem of port selection and task sequence among multiple subsystems in warehousing operation,a joint scheduling optimization model of multiple subsystems is established by adding time window constraint.Taking the warehousing operation as an example,the particle swarm optimization algorithm is used to optimize the solution,which shortens the whole batch task operation time.Based on the actual warehousing environment,the Flexsim simulation model is established and the experiment verifies the reliability of the model and its superiority over the traditional scheduling strategy.(3)In order to improve the efficiency of picking operation,shorten the time ratio of bins to and from the warehouse,introduce the buffer area,establish a picking order coupling model,sort and schedule the picking orders based on the degree of coupling between orders,and optimize by particle swarm optimization.The utility model reduces the number of times of loading and unloading of the bin,shortens the overall picking operation time,improves the picking efficiency,and selects the actual order for optimization verification.(4)The scheduling optimization model of AGVs system under fixed nodes is established.The hierarchical algorithm is designed and implemented by genetic algorithm,which effectively shortens the no-load distance between tasks and realizes the optimal solution of AGVs scheduling problem.The traditional algorithm converges faster and has a wider search depth.(5)Based on the analysis of an actual intelligent warehouse case,an intelligent warehouse scheduling system is developed on Visual Studio 2013 platform,and the validation of scheduling model and algorithm is completed.