Optimizing Freight Cost Predictions with a Hybrid GRU-GA Model: Implications for Supply Chain Sustainability

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran

2 Computational Optimization and Smart Transformation Lab, Faculty of Engineering, College of Farabi, University of Tehran, Qom, Iran

Abstract

Freight costs, being a significant percentage of the total supply chain cost, become vital for profitability and sustainability. Hence, their accurate forecast becomes highly essential to plan the supply chain efficiently. The study will consider a hybrid model using GRU neural networks in combination with a Genetic Algorithm, which seeks an optimized freight cost prediction. It captures temporal dependencies with GRUs and optimizes hyperparameters using a GA. The performance of the GRU-GA model is compared against ARIMA and AR; it has better performance and may contribute to sustainable supply chains because of better logistic optimization and the reduction of losses.

Keywords

References

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Journal of Optimization and Supply Chain Management
Volume 2, Issue 2
June 2025
Pages 111-124

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