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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.00166 |
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| _version_ | 1866914127677489152 |
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| author | Zhang, Shiman Zhou, Jinghan Yu, Zhoufan Leng, Ningai |
| author_facet | Zhang, Shiman Zhou, Jinghan Yu, Zhoufan Leng, Ningai |
| contents | To improve decision-making and planning efficiency in back-end centralized redundant supply chains, this paper proposes a decision model integrating deep learning with intelligent particle swarm optimization. A distributed node deployment model and optimal planning path are constructed for the supply chain network. Deep learning such as convolutional neural networks extracts features from historical data, and linear programming captures high-order statistical features. The model is optimized using fuzzy association rule scheduling and deep reinforcement learning, while neural networks fit dynamic changes. A hybrid mechanism of "deep learning feature extraction - intelligent particle swarm optimization" guides global optimization and selects optimal decisions for adaptive control. Simulations show reduced resource consumption, enhanced spatial planning, and in dynamic environments improved real-time decision adjustment, distribution path optimization, and robust intelligent control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Study on Supply Chain Finance Decision-Making Model and Enterprise Economic Performance Prediction Based on Deep Reinforcement Learning Zhang, Shiman Zhou, Jinghan Yu, Zhoufan Leng, Ningai Machine Learning To improve decision-making and planning efficiency in back-end centralized redundant supply chains, this paper proposes a decision model integrating deep learning with intelligent particle swarm optimization. A distributed node deployment model and optimal planning path are constructed for the supply chain network. Deep learning such as convolutional neural networks extracts features from historical data, and linear programming captures high-order statistical features. The model is optimized using fuzzy association rule scheduling and deep reinforcement learning, while neural networks fit dynamic changes. A hybrid mechanism of "deep learning feature extraction - intelligent particle swarm optimization" guides global optimization and selects optimal decisions for adaptive control. Simulations show reduced resource consumption, enhanced spatial planning, and in dynamic environments improved real-time decision adjustment, distribution path optimization, and robust intelligent control. |
| title | Study on Supply Chain Finance Decision-Making Model and Enterprise Economic Performance Prediction Based on Deep Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.00166 |