The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management
Abstract
This project addresses demand forecasting and inventory optimization in supply chain management. Traditional methods have limitations with complex demand patterns and large-scale data. Deep learning techniques are employed to enhance accuracy and efficiency. The project utilizes BO-CNN-LSTM, leveraging Bayesian optimization for hyperparameter tuning, Convolutional Neural Networks (CNNs) for spatiotemporal feature extraction, and Long Short-Term Memory Networks (LSTMs) for modeling sequential data. Experimental results validate the effectiveness of the approach, outperforming traditional methods. Practical implementation in supply chain management improves operational efficiency and cost control.
Related Content
Ke Zheng, Zhou Li.
© 2024.
21 pages.
|
Weihui Han, Tianshuo Zhang, Jamal Khan, Lujian Wang, Chao Tu.
© 2024.
22 pages.
|
Chen Quan, Baoli Lu.
© 2024.
22 pages.
|
Peijin Li, Xinyi Peng, Chonghui Zhang, Tomas Baležentis.
© 2024.
25 pages.
|
Lei Zhao, Bowen Deng, Liang Wu, Chang Liu, Min Guo, Youjia Guo.
© 2024.
27 pages.
|
Xiaoye Ma, Yanyan Li, Muhammad Asif.
© 2024.
29 pages.
|
Hao Wu, Zhiyi Zhang, Zhilin Zhu.
© 2024.
12 pages.
|
|
|