Cargo Volume Forecasting Analysis Based on Simulated Annealing Algorithm and SARIMA Modeling

DOI

10.2991/978-94-6463-702-1_100How to use a DOI?

Keywords

Warehouse allocation strategy; time series; linear regression; SARIMA model; simulated annealing algorithm

Abstract

This study uses an integrated SARIMA model and a simulated annealing algorithm dedicated to exploring the forecasting of goods sales and inventory levels in the e-commerce industry, as well as the optimization of warehouse allocation strategies. First, this paper uses a composite model based on coupled time series and linear regression and a SARIMA model to forecast the inventory and sales volume of multiple categories in future months by analyzing historical data and identifying trends and seasonal patterns. Then, the study uses a simulated annealing algorithm to find an optimal warehouse allocation strategy that allows for maximum utilization of warehouse capacity and production capability under the condition that each category can only be stored in one warehouse.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Saiyang Zhang
PY  - 2025
DA  - 2025/05/05
TI  - Cargo Volume Forecasting Analysis Based on Simulated Annealing Algorithm and SARIMA Modeling
BT  - Proceedings of the 2025 10th International Conference on Financial Innovation and Economic Development (ICFIED 2025)
PB  - Atlantis Press
SP  - 950
EP  - 958
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-702-1_100
DO  - 10.2991/978-94-6463-702-1_100
ID  - Zhang2025
ER  -