Try this Jupyter Notebook Modeling Example to learn how to solve a classic supply network design problem that involves finding the minimum cost flow through a network. We’ll show you how – given a set of factories, depots, and customers – you can use mathematical optimization to determine the best way to satisfy customer demand while minimizing shipping costs.
This model is example 19 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 273-275 and 330-332.
This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models.
Take your supply chain network design skills to the next level in this example. We’ll show you how – given a set of factories, depots, and customers – you can use mathematical optimization to determine which depots to open or close in order to minimize overall costs.
This model is example 20 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 275-276 and 332-333
This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models.
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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