Reinforcement Learning for Supply Chain Management

Reinforcement Learning for Supply Chain Optimization Projects

What you’ll learn
✓ Build and train reinforcement learning agents using Python and relevant ML libraries
✓ Evaluate and compare RL policies against traditional supply chain heuristics
✓ Develop RL-based solutions for dynamic pricing and transportation decision-making
✓ Design and optimize inventory control policies under stochastic demand and lead times

Requirements
● Basic knowledge of Python programming
● Familiarity with fundamental probability and statistics concepts
● Introductory understanding of machine learning concepts is helpful but not mandatory
● A computer capable of running Python (Anaconda or similar environment recommended)
● Willingness to work with mathematical formulations and hands-on coding exercises

Description
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.

Who this course is for
■ Supply chain professionals who want to leverage advanced AI methods for operational optimization
■ Data scientists and machine learning engineers interested in real-world RL applications
■ Industrial engineers and operations researchers seeking modern decision-making approaches
■ Graduate students in supply chain, operations, or data science programs
■ Professionals aiming to bridge the gap between reinforcement learning theory and supply chain practice

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