Tariff-Sensitive Global Supply Chains: Semi-Markov Decision Approach with Reinforcement Learning

Global supply chains often face uncertainties in production lead times, fluctuating exchange rates, and varying tariff regulations, all of which can significantly impact total profit. To address these challenges, this study formulates a multi-country supply chain problem as a Semi-Markov Decision Process (SMDP), integrating both currency variability and tariff levels. Using a Q-learning-based method (SMART), we explore three scenarios: (1) wide currency gaps under a uniform tariff, (2) narrowed currency gaps encouraging more local sourcing, and (3) distinct tariff structures that highlight how varying duties can reshape global fulfillment decisions. Beyond these baselines we analyze uncertainty-extended variants and targeted sensitivities (quantity discounts, tariff escalation, and the joint influence of inventory holding costs and tariff costs). Simulation results, accompanied by policy heatmaps and performance metrics, illustrate how small or large shifts in exchange rates and tariffs can alter sourcing strategies, transportation modes, and inventory management. A Deep Q-Network (DQN) is also applied to validate the Q-learning policy, demonstrating alignment with a more advanced neural model for moderate-scale problems. These findings underscore the adaptability of reinforcement learning in guiding practitioners and policymakers, especially under rapidly changing trade environments where exchange rate volatility and incremental tariff changes demand robust, data-driven decision-making.