Introduction 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 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.
Access the Jupyter Notebook Modeling Example
Click on the button below to access the example in Google Colab, which is a free, online Jupyter Notebook environment that allows you to write and execute Python code through your browser.
How to Run the Example
-To run the example the first time, choose “Runtime” and then click “Run all”.
-All the cells in the Jupyter Notebook will be executed.
-The example will install the gurobipy package, which includes a limited Gurobi license that allows you to solve small models.
-You can also modify and re-run individual cells.
-For subsequent runs, choose “Runtime” and click “Restart and run all”.
-The Gurobi Optimizer will find the optimal solution of the modeling example.
Check out the Colab Getting Started Guide for full details on how to use Colab Notebooks as well as create your own.
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