The United Nations World Food Program (WFP) supplies food assistance to around 100 million people in 80 countries each year. Transporting food in a global transportation network is a challenging undertaking. In this notebook, we will build an optimization model to set up a food supply chain based on real data from WFP.
The idea is to transport food from cities identified as “suppliers” to beneficiary cities in Syria. The quantity of food arriving at beneficiary cities must be sufficient enough to satisfy basic nutritional needs of the people there. On the other hand, the procurement and transportation of food comes at a cost, and therefore must be done efficiently. In this notebook, we will learn how to set up an optimization problem to achieve a cost-efficient food supply chain.
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.
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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