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

In: Proceedings of the 2025 10th International Conference on Financial Innovation and Economic Development (ICFIED 2025)

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  • Saiyang Zhang

    (Hebei Finance University, School of Big Data Science)

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.

Suggested Citation

  • Saiyang Zhang, 2025. "Cargo Volume Forecasting Analysis Based on Simulated Annealing Algorithm and SARIMA Modeling," Advances in Economics, Business and Management Research, in: Maizaitulaidawati Md Husin & Tomoki Fujii & Xiaodong Lai & Azlina Binti Md Yassin (ed.), Proceedings of the 2025 10th International Conference on Financial Innovation and Economic Development (ICFIED 2025), pages 950-958, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-702-1_100
    DOI: 10.2991/978-94-6463-702-1_100
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