You can generate multiple scenarios to test your model against a wide range of data and understand how robust the model is.

This example steps you through the process to generate multiple scenarios with a model. This makes it possible to test the performance of the model against multiple randomly generated data sets. It's important in practice to check the robustness of a model against a wide range of data. This helps ensure that the model performs well in potentially stochastic real-world conditions.

The example is the StaffPlanning model in the DO-samples.

The example is structured as follows:

• The model StaffPlanning contains a default scenario based on two default data sets, along with five additional scenarios based on randomized data sets.
• The Python notebook CopyAndSolveScenarios contains the random generator to create the new scenarios in the StaffPlanning model.

For general information about scenario management and configuration, see Scenarios in a Decision Optimization experiment.

For information about writing methods and classes for scenarios, see the Decision Optimization Client Python API documentation.