Table of contents

Working with multiple scenarios

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 dsx-samples project.

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 more information on working with scenarios, see the dd-scenario Python API documentation.