Deep Reinforcement Learning for Supply Chain Optimization: A DQN and LSTM-Based Approach

Abstract: Effective inventory management is essential for optimizing supply chains, balancing stock levels, minimizing holding costs, and preventing stockouts. Traditional forecasting andrule-basedsystemsoftenfailtoadapttoreal-timede- mandfluctuations and supplyuncertainties.Inthisresearch, we propose a Reinforcement Learning (RL)-based approach for dynamic inventory optimization, leveraging Deep Q-Networks (DQN)alongsideMulti-ArmedBandit(MAB)strategiessuch as Epsilon-Greedy, Upper Confidence Bound (UCB), KL-UCB, and Thompson Sampling. The DQN agent learns an optimal replenishment policybyinteracting withtheenvironmentandad- justing inventory decisions based on observed demand patterns. Ourexperimentalanalysiscomparesthesetechniquesbased on key performance metrics such as inventory costs, stockout rates, and supply chain efficiency. Results indicate that while bandit-based methods provide strong baseline heuristics, DQN significantly outperforms them in long-term adaptability and decision-making under uncertainty. These findings highlight the potential of deep reinforcement learning to enhance real-time demand responsiveness, reduce operational costs, and improve supply chain resilience