用深度强化学习优化供应链决策，提升企业绩效预测与路径规划效率。
Study on Supply Chain Finance Decision-Making Model and Enterprise Economic Performance Prediction Based on Deep Reinforcement Learning
- 结合深度学习与智能粒子群算法，实现全局优化决策。
- 仿真显示资源消耗降低，动态环境下决策响应更快、路径更优。
- 适合研究智能供应链管理与企业经济预测的学者与从业者。
为提升后端集中式冗余供应链的决策与规划效率，本文提出一种融合深度学习与智能粒子群优化的决策模型。构建了分布式节点部署模型与最优规划路径，利用卷积神经网络从历史数据中提取特征，线性规划捕捉高阶统计特征。通过模糊关联规则调度与深度强化学习优化模型，神经网络拟合动态变化。采用“深度学习特征提取-智能粒子群优化”混合机制，实现全局优化并选择自适应控制最优决策。仿真结果表明，该模型可降低资源消耗，提升空间规划能力，在动态环境中显著改善实时决策调整、配送路径优化与鲁棒智能控制性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。