Linear programming is a powerful tool that uses mathematics to solve business problems. It's a versatile approach that can be used to address a wide range of issues, from logistics and scheduling to resource allocation and location problems. Industries across the spectrum leverage linear programming to tackle complex business challenges.

About the Linear Programming Tutorial

This tutorial series is designed to provide you with a comprehensive understanding of linear programming. Whether you're a data scientist, computer scientist, systems/IT engineer, or business analyst, this series will show you how to translate real-world problems into mathematical models for various applications across industries.

Why Learn Linear Programming?

Linear programming is widely used by Fortune 100 companies, including tech giants like Apple and Google, retail behemoth Walmart, and leading airlines like Air France and Lufthansa. These companies use linear and mixed-integer linear programming to optimize their operational efficiency. For example, airlines use mathematical programming for flight routing, staffing, and maintenance, while professional sports leagues like the NFL use it for game scheduling. Manufacturing companies also use it for procurement, production, and distribution planning.

Tutorial Prerequisites

To get the most out of this series, you'll need a basic understanding of linear algebra and calculus at the college level, familiarity with mathematical notation, and a basic knowledge of Python.

What to Expect

Throughout this series, we'll emphasize how to build linear programming models that capture the key features of a real-life business problem, so it can be solved fast enough that you can take action.

While the series is pragmatic in nature, we'll also cover the theoretical aspects of linear programming, so you can build and fine-tune efficient mathematical optimization models.

Resources

Download the complete Linear Programming Tutorial Series slide deck.

View the entire series:

1. Welcome: Linear Programming Tutorial

2. Chapter 1: Mathematical Programming

3. Chapter 2: Introduction to Linear Programming

4. Chapter 3: Mixed Integer Linear Programming Problems

5. Chapter 4: Furniture Factory Problem

6. Chapter 5: Simplex Method

7. Chapter 6: Modeling and Solving Linear Programming Problems

8. Chapter 7: Sensitivity Analysis of Linear Programming Problems

9. Chapter 8: Multiple Optimal Solutions

10. Chapter 9: Unbounded Linear Programming Problems

11. Chapter 10: Infeasible Linear Programming Problems

12. Chapter 11: Linear Programming Overview - Further Considerations

13. Chapter 12: Duality in Linear Programming

14. Chapter 13: Optimality Conditions

15. Chapter 14: Dual Simplex Method