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  4. Vol 8, Issue 3, 2024

Research on Logistics Network Based on ARIMA Prediction Model and Multi-Objective Optimization

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DOI: 10.23977/acss.2024.080301 | Downloads: 2 | Views: 59

Author(s)

Wei Guo 1, Jing Zhao 1, Dianzhe Yang 1, Canglong Zhang 1, Ping Li 1

Affiliation(s)

1 College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, China

Corresponding Author

Wei Guo

ABSTRACT

This paper carries out a profound research on the problem of emergency transportation and structure optimization of goods in logistics network. First of all, a prediction model based on the transportation volume of historical routes is established, and data preprocessing is carried out for logistics routes and cargo volume data. Then, the ARIMA model is selected for prediction, and the daily cargo volume of each route is trained and tested during the period from 2023-01-01 to 2023-01-31, and then the ARIMA model is evaluated and adjusted by the white noise test. The ARIMA model is used to predict the future line cargo volume of DC14→DC10, DC20→DC35, DC25→DC62, and the results are analyzed and interpreted, and the prediction results are presented in the paper in the form of a three-line table. Next, on the basis of the previous prediction, with the objectives of minimizing the number of changes in cargo volume before and after the closure of DC5 and making all parcels flow as quickly and normally as possible, we introduce constraints such as the workload being balanced as much as possible, maximum cargo volume constraints, non-negative constraints, total volume constraints, etc., establish a bi-objective non-linear integer planning model, and use simulated annealing algorithm to solve the model.

KEYWORDS

ARMA, multi-objective optimization, logistics network, emergency movement, simulated annealing

CITE THIS PAPER

Wei Guo, Jing Zhao, Dianzhe Yang, Canglong Zhang, Ping Li, Research on Logistics Network Based on ARIMA Prediction Model and Multi-Objective Optimization. Advances in Computer, Signals and Systems (2024) Vol. 8: 1-8. DOI: http://dx.doi.org/10.23977/acss.2024.080301.

REFERENCES

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