Companies across numerous industries – including petrochemical refining, wastewater treatment, and mining – use mathematical optimization to solve the pooling problem. In this example, we’ll guide you through the process of building a mixed-integer quadratically-constrained programming (MIQCP) model of a pooling problem using the Gurobi Python API and show you how to generate an optimal solution to the problem with the Gurobi Optimizer.
This modeling example is at the advanced level, where we assume that you know Python and the Gurobi Python API and that you have advanced knowledge of building mathematical optimization models. Typically, the objective function and/or constraints of these examples are complex or require advanced features of the Gurobi Python API.
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.
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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