Overview
Discover how mathematical optimization outperforms LLMs for complex scheduling tasks, and learn to use Timefold Solver for reliable AI-powered scheduling solutions without complex math.
Syllabus
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- **Introduction to Scheduling and AI**
-- Overview of scheduling challenges
-- Brief introduction to AI and optimization
- **Understanding Mathematical Optimization**
-- Fundamentals of mathematical optimization
-- Key differences between LLMs and optimization algorithms
- **Limitations of Language Models in Scheduling**
-- Capabilities of LLMs
-- Why LLMs struggle with complex scheduling
- **Introduction to Timefold Solver**
-- What is Timefold Solver?
-- Key features and benefits
- **Setting Up Timefold Solver**
-- Installation and setup process
-- Basic configuration
- **Modeling Scheduling Problems**
-- Defining objectives and constraints
-- Common scheduling use cases
- **Solving Scheduling Problems with Timefold**
-- Step-by-step process of problem-solving with Timefold
-- Understanding outputs and results
- **Advanced Topics in Scheduling Optimization**
-- Handling dynamic and real-time changes
-- Integrating Timefold with other AI tools
- **Case Studies and Real-World Applications**
-- Successful implementations of Timefold Solver
-- Lessons learned from real-life scheduling scenarios
- **Best Practices and Tips**
-- Common pitfalls in scheduling optimization
-- Optimizing performance and accuracy
- **Hands-On Project**
-- Apply Timefold to a sample scheduling problem
-- Present solutions and discuss outcomes
- **Conclusion and Next Steps**
-- Recap of key learning points
-- Resources for further learning and exploration of scheduling optimization.
Taught by
Tags
On-Demand
