Study on Supply Chain Finance Decision-Making Model and Enterprise Economic Performance Prediction Based on Deep Reinforcement Learning

TL;DR

This paper develops a deep reinforcement learning-based decision-making model for supply chain finance that integrates deep learning, particle swarm optimization, and fuzzy rules to enhance efficiency and adaptability.

Contribution

It introduces a novel hybrid model combining deep learning, particle swarm optimization, and fuzzy rules for supply chain decision-making and enterprise performance prediction.

Findings

Reduced resource consumption in supply chains

Improved real-time decision adjustment in dynamic environments

Enhanced spatial planning and distribution path optimization

Abstract

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.