Logistics and Supply Chain Management (SCM) are both key areas in modern industry and commerce which need better route planning and warehouse optimization. Traditional methods that are in practice have often fall short in of addressing the dynamic complexities of modern logistics which results in inefficient travel times, fuel consumption, and space utilization. To counter these limitations this study introduces an integrated model that combines Long Short-Term Memory (LSTM) networks, Radial Basis Function Neural Networks (RBNN), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for route planning and warehouse optimization. The proposed model employ LSTM to predict traffic patterns and RBNN to optimize space utilization in warehouse. The NSGA-II model is then utilized for multi-objective optimization of minimizing travel times and maximizing warehouse space utilization. In experiment analysis the proposed model achieved the highest accuracy and least variability in predictions, with mean MAE, RMSE, and MAPE values of 0.57, 1.12, and 5.9%, respectively.
Keywords
Supply Chain Management, LSTM, Radial Basis Function Neural Networks, Machine Learning, MAE, RMSE and MAPE.
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Shiyu Tian
Shiyu Tian
Business School, Marketing, Edutus Egyetem, Budapest, Hungary.
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Cite this article
Shiyu Tian and Jianghua Luo, “Enhancing International Logistics and Supply Chain
Management: Deep Learning Strategies for Enhanced Route Planning and Warehouse Optimization”,
Journal of Machine and Computing, pp. 943-952, October 2024. doi:10.53759/7669/jmc202404087.
