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Jupyter Notebook

Airline Planning After Flight Disruption


Weather events are a major threat to the airline industry. The unpredictable nature of snowstorms, heavy rains, and icy runways make it difficult for aviation planners to make accurate schedules.



 

This notebook walks through the optimization problem of deciding which flights to operate and which flights to cancel after a weather disruption. We do this by constructing a mathematical optimization model that reduces the revenue lost from the cancelled flights. In this example, we are using real data in France compiled by Amadeus.

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.

 

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. 

Airline Planning After Flight Disruption

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.