Intro to Mathematical Optimization Modeling

Are you looking to learn the basics of mathematical optimization modeling? If so, then this is a great place to start. In this tutorial, we’ll walk you through the process of building a mathematical optimization model and solving a mathematical optimization problem. We’ll begin by giving you an overview of the key components of a simple mathematical optimization problem, then show you how to create a mathematical optimization model (or, to be more precise, a mixed-integer programming or MIP model) of the problem using the Gurobi Python API, and then demonstrate how you can automatically generate an optimal solution using the Gurobi Optimizer.

This modeling tutorial is at the introductory level, where we assume that you know Python and that you have a background on a discipline that uses quantitative methods.

You may find it helpful to refer to the documentation of the Gurobi Python API. This notebook is explained in detail in our series of tutorial videos on mixed-integer linear programming. You can watch these videos by clicking here.

What Is Optimization Modeling?

Optimization modeling is the process of translating complex decision problems into mathematical models that can be solved with an optimization solver, like Gurobi. An optimization model consists of an objective function (the thing you want to maximize or minimize, like profits or costs) and a set of constraints (the rules your solution must satisfy, like staying within your limited number of available resources).

Why Learn Optimization Modeling?

The demand for optimization skills is on the rise. In fact, according to the Bureau of Labor Statistics, an estimated 28,300 new operations research jobs will be created by 2033. By learning optimization modeling, you can invest in your career while helping to solve some of the world’s most complex challenges.