Optimize with GA & RL
About this course
Ready to transform your optimization skills with cutting-edge AI? This Short Course was created to help data analysis professionals accomplish advanced optimization in inventory management and supply chain decision-making. By completing this course, you'll master genetic algorithms for inventory problems, implement Q-learning agents for supply chain simulations, and fine-tune parameters for optimal performance. You'll gain hands-on experience comparing heuristic methods with traditional approaches and evaluating exploration-exploitation trade-offs. By the end of this course, you will be able to: Apply genetic algorithms to inventory-replenishment problems Train Q-learning agents in grid-world supply-chain simulations Evaluate convergence speed vs. solution quality trade-offs Optimize ε-greedy parameters for reinforcement learning performance This course is unique because it bridges theoretical optimization concepts with practical supply chain applications using real-world datasets and industry-standard tools. To be successful in this project, you should have programming experience with Python and basic knowledge of optimization principles.
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What you'll learn
- Apply genetic algorithms to inventory-replenishment problems
- Train Q-learning agents in grid-world supply chain simulations
- Evaluate convergence speed vs. solution quality trade-offs
- Optimize ε-greedy parameters for reinforcement learning performance
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