Try this example to learn how to use mathematical optimization to tackle a common, but critical agricultural pricing problem: Determining the prices and demand for a country’s dairy products in order to maximize total revenue derived from the sales of those products. You will learn how to model this problem as a quadratic optimization problem using the Gurobi Python API and solve it using the Gurobi Optimizer.
This model is example 21 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 276-278 and 333-335.
This modeling example is at the intermediate level, where we assume that you know Python and are familiar with the Gurobi Python API. In addition, you should have some knowledge about building mathematical optimization models.
Access the Jupyter Notebook Modeling Example
Click on the link 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.
Agricultural Pricing
How to Run the Jupyter Notebook Modeling 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 “on “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.
