Author(s)

Yizi Huang ¹, Gen Leng ¹

Affiliation(s)

¹ Department of Computer and Information Engineering, Hubei University, Wuhan 430062, China

Corresponding Author

Yizi Huang

ABSTRACT

In order to reduce over-dependence on a single supplier, manufacturers often choose multiple suppliers to supply the same raw material and will commission forwarders to transport the goods, increasing the complexity of the supply chain system. This paper therefore focuses on establishing a time series forecasting and multi-objective planning model to provide a collaborative decision for ordering forwarding solutions to improve the production efficiency and stability of the enterprise. We first calculated the minimum number of suppliers to meet the production demand and used the ARIMA time series forecasting model to obtain the weekly supply quantities for the next 24 weeks using the weekly supply quantities of each supplier for the past 5 years as input to the model. In addition, based on the supplier supply capacity values from problem 1, we set the supplier selection priority, and based on this, we set up a multi-objective planning model for an enterprise's ordering and forwarding solution with the objective of minimizing the weekly ordering cost of raw materials and minimizing the loss in the forwarding process, and finally obtained the minimum number of suppliers to meet the production demand as 28, and gave the best weekly ordering and forwarding solution for the next 24 weeks.

KEYWORDS

ordering transit decisions, time series forecasting, multi-objective planning

CITE THIS PAPER

Yizi Huang, Gen Leng, Ordering transit strategy based on time series forecasting and multi-objective planning. Advances in Computer, Signals and Systems (2022) Vol. 6: 33-40. DOI: http://dx.doi.org/10.23977/acss.2022.060208.

REFERENCES

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[2] Li Dan. Research on the construction and application of supplier evaluation system of Company A [D]. South China University of Technology,2018.
[3] Zhai Yue, Wang Tienan, Qu Lu, Wang Banqiao, Wang Kang. Construction risk evaluation of deep foundation pits in underground complexes based on IFS-dynamic weighting[J]. Safety and Journal of the Environment,2021,21(04):1389-1396.
[4] Gao W, Zhang Q P, Dun X B, et al. Comprehensive assessment of advanced military aerospace technologies based on improved EAHP and dynamic weighting[J]. Systems engineering and electronics, 2016.
[5] Yang H-M, Pan Z-Song, Bai Wei. A review of time series forecasting methods[J]. Computer Science, 2019,46(01):21-28. 
[6] Li C-Chao, Liu S. Comparison of forecasting based on ARIMA model, grey model and regression model [J]. Statistics and Decision Making,2019,35(23):38-41. 

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