Research On Flue Optimization Of Cigarettes Automatic Picking Line Based On Forecast Of Cigarettes Sku Sales In W City

Cigarette automatic picking line has greatly improved the picking speed and quality of cigarette logistics distribution center.With the accelerating trend of intelligent cigarette automatic picking equipment,a new requirement of "cost reduction and efficiency increase" is put forward for the design and planning of cigarette automatic picking line.Reasonable distribution of flue in cigarette automatic picking line not only affects the picking efficiency of cigarette logistics distribution center,but also indirectly affects the logistics benefit and service level of the whole logistics link of the distribution center.In order to speed up the construction and development of intelligent logistics in the tobacco industry and realize the dynamic optimization of the picking link of cigarette logistics distribution center,flue optimization of picking line plays a pivotal role in improving the efficiency of automatic cigarette picking line.How to use scientific models and methods,combined with the actual situation of the construction of automatic cigarette picking line in W City,put forward a reasonable flue optimization scheme,is currently facing a problem of important practical significance.Under the background of intelligent picking advocated by tobacco National Bureau,this paper studies the optimization of cigarette flue in automatic picking line for predicting cigarette product sales volume in intelligent picking.Based on forecast of cigarettes sku sales,the paper designs a simple and easy method for the rational configuration of the picking equipment in the automatic cigarette picking line and the dynamic optimization of the flue of the picking line.First of all,according to the real sales orders of cigarettes in W City,based on the study of historical sales fluctuations of cigarettes,the traditional ARIMA time series prediction method is used to process linear data,and the SVM prediction method is used to process nonlinear data.Finally,the ARIMA+SVM combined prediction model is constructed.In order to achieve better prediction effect,a more accurate prediction of cigarette picking quantity in the future time is made.Secondly,based on the existing data and prediction results,combined with the actual situation of the picking system of the cigarette logistics distribution center in W City,optimized the distribution design of cigarette product regulation flue of the automatic cigarette picking line.Using EIQ-ABC quantitative analysis method,the order data of cigarette logistics and distribution center were analyzed quantitatively,and the automatic picking equipment of cigarette with different product specifications was determined preliminatively.On this basis,the operation time model of cigarette automatic picking system was analyzed,and the shortest total picking time of order was the goal.After transformation,the flue optimization model of cigarette automatic picking line with product regulation similarity was established.Finally,a real cigarette order picking data of a certain day in the cigarette logistics distribution center of W City was introduced to solve and verify the model.The traditional dynamic clustering algorithm was used for reference,and the improved K-means dynamic clustering solution algorithm was combined according to the characteristics of the model.Finally,it is verified that the picking efficiency can be increased by 11%?19% under different quantity of product regulations.