Parallel Distribution Center Order Picking Optimization Of Batch

Today. the rapid development of information technology, mobile Internet,E-commerce as a new business model fully integrated into all aspects of people’s lives.The distribution center is the core of e-commerce can be achieved, The development ofe-commerce is also a bottleneck. In distribution centers. Order picking is in accordancewith the information on the order entry of goods from the shelves detection,classification, concentration, packaging, boxing operation process. Throughout theprocess, the most time-consuming and labor activity is picking the highest. Especiallyin recent years, customer order gradually to diversify the direction of the developmentof small quantities, Research on the operational efficiency of picking logistics supplychain has become a hot research field, therefore, the operation of the picking processoptimization to improve distribution efficiency is important.This paper studies the model of picking orders in batches, batch picking paralleloptimization. First, a certain amount of orders by the total amount of the batch, whenthe batch window, orders in batches, intelligent batch model comparison. Model uses asmart batch saving algorithm in order to calculate the path, design genetic algorithm,In seeking to meet the constraints of local optima conditions, Travel time is reduced sopicking improve picking efficiency. Secondly, the model is built up in batches forfurther improvements in parallel, the main type of parallel model, Fine-grained model,master-slave model and coarse-grained model. Select the master-slave model forparallel improvements, A lesser amount of the population by way of a batchinitialization parallel computing, Orders are various populations in batches, each batchto rely on their own transport operator changes pass all the information, So that eachbatch to achieve optimal synergy, By artificial selection coefficients save the bestindividual for each population, Achieve better convergence degree, Improve the accuracy of the optimal solution.According to the paper batches strategy designed to optimize picking and parallelgenetic targets, Design strategies and test parallel genetic algorithm simulationverification, The results show that for the selection and application of methods andprovides the basis for improved efficiency.