Toward Intelligent Supply Chains: A Reinforcement Learning–Based Approach

This paper proposes a reinforcement learning–based approach toward intelligent supply chains capable of adaptive and data-driven decision-making in dynamic environments. Traditional supply chain optimization methods often rely on static assumptions and predefined rules, which limit their effectiveness under uncertainty. In contrast, the proposed framework models supply chain operations as a sequential decision-making process, allowing an agent to learn optimal policies through continuous interaction with the environment. Key operational components, including inventory control, transportation planning, and demand fulfillment, are integrated into a unified reinforcement learning model. Simulation-based experiments demonstrate that the proposed approach outperforms conventional optimization and rule-based methods in terms of total operational cost, service level, and system adaptability. The results indicate that reinforcement learning provides a promising foundation for building intelligent supply chains that can autonomously respond to changing operational conditions. Each supply chain entity, such as suppliers, warehouses, and distributors, is modeled as an autonomous learning agent that interacts with other agents and the shared environment. Through cooperative learning, the agents gradually develop coordinated policies that improve overall system performance. The proposed approach addresses the limitations of centralized decision-making by enabling decentralized yet coordinated control. The learning-based framework enables supply chain systems to adapt decisions dynamically without explicit mathematical modeling of uncertainties. Experimental results obtained from simulated logistics scenarios show that deep reinforcement learning significantly improves decision quality compared to traditional heuristics, particularly in volatile environments.