Explore our modeling examples for the Gurobi Python API
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.
For details on licensing or on running the notebooks, see the overview on Modeling Examples
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