Supply chains operate in highly dynamic and uncertain environments. Traditional optimization methods often struggle when decisions must be made sequentially under uncertainty. Reinforcement Learning (RL) provides a powerful framework to address these challenges by enabling systems to learn optimal policies through interaction with the environment.
In this course, you will learn how to apply reinforcement learning techniques specifically to supply chain management problems. We begin with the foundations of RL, including Markov Decision Processes (MDPs), reward design, value functions, and policy optimization. You will then explore core algorithms such as Q-Learning, Deep Q-Networks (DQN), and Policy Gradient methods, with a strong focus on practical implementation.
The course connects theory directly to real supply chain applications. You will model and solve problems such as single- and multi-echelon inventory control, demand uncertainty management, safety stock optimization, dynamic pricing, transportation decisions, and capacity planning. Throughout the course, we emphasize how to properly define states, actions, rewards, and constraints in operational environments.
Hands-on coding exercises will guide you in building RL agents step by step, training them, and evaluating their performance against traditional policies. You will also learn how to interpret results, avoid common pitfalls, and scale solutions to more complex systems.
By the end of this course, you will be able to design and implement reinforcement learning solutions tailored to real-world supply chain challenges, bridging the gap between machine learning theory and operational decision-making.
