Cost Optimization Method for Procurement and Inventory of NEV Manufacturers Based on Deep Reinforcement Learning

Abstract

The new energy vehicle (NEV) supply chain faces significant challenges stemming from highly uncertain end-user demand and sharp fluctuations in key raw material prices. These factors make procurement costs and inventory levels difficult to control, directly impacting supply chain stability and profitability. Traditional methods, such as stochastic dynamic programming (DP) and standard reinforcement learning (RL) models, which primarily respond only to historical and current state information, often prove insufficient for effectively addressing these complexities. To address these limitations, this paper proposes a Proactive Reinforcement Learning (Pro-RL) framework for joint procurement and inventory decision-making. By integrating a predictive information module into the sequential decision-making process of a Soft Actor-Critic (SAC) agent, the framework constructs an enhanced state space that incorporates predicted future information. This allows the agent to move beyond traditional passive response patterns, enabling proactive utilization of market information to achieve a better balance between immediate costs and long-term risks through iterative learning. To validate its effectiveness, this study develops a supply chain simulation platform aligned with NEV industry characteristics, and comparisons with multiple benchmarks were conducted. Experimental results demonstrate that this end-to-end decision-making policy, which integrates predictive information with deep reinforcement learning, offers advantages in responding to market volatility and achieving coordinated optimization of cost and service levels. This provides NEV enterprises with a theoretical model and practical approach for building flexible and efficient smart supply chains.