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:
Welcome: Linear Programming Tutorial
Chapter 1: Mathematical Programming
Chapter 2: Introduction to Linear Programming
Chapter 3: Mixed Integer Linear Programming Problems
Chapter 4: Furniture Factory Problem
Chapter 5: Simplex Method
Chapter 6: Modeling and Solving Linear Programming Problems
Chapter 7: Sensitivity Analysis of Linear Programming Problems
Chapter 8: Multiple Optimal Solutions
Chapter 9: Unbounded Linear Programming Problems
Chapter 10: Infeasible Linear Programming Problems
Chapter 11: Linear Programming Overview - Further Considerations
Chapter 12: Duality in Linear Programming
Chapter 13: Optimality Conditions
Chapter 14: Dual Simplex Method
