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 Scenario pane and Overview.

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