Building AI Solutions with Google OR-Tools

Overview

Learn to build AI solutions using Google OR-Tools for optimization problems. Explore constraint programming, linear programming, and mixed-integer programming through practical examples like Sudoku and scheduling.

Syllabus

• Introduction to Google OR-Tools
Overview of AI optimization tools
Installation and setup of OR-Tools
• Understanding Optimization Problems
Definition and types of optimization problems
Use cases and applications in AI
• Constraint Programming with OR-Tools
Basics of constraint programming
Building a constraint satisfaction model
Practical example: Solving a Sudoku puzzle
• Linear Programming with OR-Tools
Introduction to linear programming
Formulating linear problems
Practical example: Simple resource allocation
• Mixed-Integer Programming with OR-Tools
Concepts of mixed-integer programming
Formulating and solving MIP problems
Practical example: Scheduling and timetabling
• Advanced Features and Techniques
Customizing solver parameters
Integrating OR-Tools with Python for enhanced capabilities
• Case Studies and Real-world Applications
Comprehensive example: Developing a workforce scheduling solution
Analyzing and optimizing logistics and operations
• Best Practices and Optimization Strategies
Common pitfalls and how to avoid them
Performance tuning and scalability considerations
• Conclusion and Future Directions
Emerging trends in optimization and AI
Further resources and continued learning paths
• Final Project
Students will build an AI solution using OR-Tools to address an optimization problem of their choice.

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Subjects

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