| Title |
Research on the Implementation of Green Supply Chain Management System Based on Hyperparameter Optimization Neural Network Combination Model |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.5.642 |
| Keywords |
Green supply chain management; Combinatorial model of neural network; Hyperparameter optimization; Predictive accuracy |
| Abstract |
This study explores the implementation of a green supply chain management system using a hyperparameter-optimized neural network model. By integrating neural network algorithms and optimization techniques, the study enhances prediction accuracy and efficiency. An in-depth analysis identifies key green supply chain components and addresses shortcomings in existing frameworks through a composite model architecture.
This study meticulously constructs a model capable of navigating intricate supply chain data by incorporating a diverse array of neural network algorithms, notably Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs). Subsequently, to refine the model’s parameters and achieve peak performance, sophisticated hyperparameter optimization techniques, including mesh search and Bayesian optimization, are employed.
This comprehensive approach ensures that the model is tailored to excel in managing the complexities inherent in supply chain data analysis. The green supply chain management system, enhanced by a hyperparameter-optimized neural network model, demonstrates high predictive accuracy and resilience. It effectively forecasts environmental risks, energy consumption, and resource use while offering decision support for managers. This study introduces a new technical approach, strongly supporting sustainable enterprise development and green supply chain transformation. |