Cao Qingyun. Logistics Forecast And Packaging Art Design Based on Grey Markov Model. Dynamic Systems and Applications 29 (2020) No. 5, 1904 – 1913

https://doi.org/10.46719/dsa202029523

ABSTRACT.
Logistics market analysis provides a comprehensive overview of business models, key strategies, and respective market stocks for some of the most important roles in this landscape. These courses are conducted entirely with in-depth reviews, focusing on impact factors, market point revenue, segment data, regional law data, and national wise data. Logistics demand forecasting plays an important role in resource optimization and corporate competition. Gray forecast model functions as a low sample requirement and high forecast accuracy. It is suitable for demanding logistics as expected. But the prediction system complexity can block its advertisement and applications. We proposed the Logistics Packaging Gray Markov model (LPGMM) for logistics and packaging forecasting of Material flow forecasting is an important content in the field of programming, management, and logistics. Integrating the LPGMM for the logistics industry to take quantitative logistics in certain logistics sectors over the next few years, predict the logistics volume and rate it. The results show that the model is more accurate and reliable than the gray model. Analyze Factors areanalyzing the design, density, contrast, and color variation Product Packaging goods transfer Cost. In the functions, the ability to quickly integrate the radial base probability of the packaging, modeling, and prediction methods can truly learn the code information and predict the logistic flow cost. True prediction, the method is possible and effective.

Keywords: Logistics factors, Gray Markov model, Data analysis, Forecasting Accuracy, Material flow, predict the modeling. Logistic packaging -LPGMM