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Generating multiple Decision Optimization scenarios
Last updated: Dec 05, 2024
Decision Optimization notebook generating multiple scenarios

This tutorial shows you how to generate multiple scenarios from a notebook using randomized data. Generating multiple scenarios lets you test a model by exposing it to a wide range of data.

Before you begin

Requirements
To edit and run Decision Optimization models, you must have the following prerequisites:
Admin or Editor roles
You must have Admin or Editor roles in the project. Viewers of shared projects can only see experiments, but cannot modify or run them
watsonx.ai Runtime service
You must have a watsonx.ai Runtime service that is associated with your project. You can add one when you create a Decision Optimization experiment.
Deployment space
You must have a deployment space that is associated with your Decision Optimization experiment. You can choose a deployment space when you create a Decision Optimization experiment.

About this task

The files used in this example are in the DO-samples project. The model concerned is StaffPlanning and the notebook is CopyAndSolveScenarios.

Procedure

To create and solve a scenario using a sample:

  1. Download and extract all the DO-samples on to your machine. You can also download just the StaffPlanning.zip file from the Model_Builder subfolder for your product and version, but in this case do not extract it.
  2. Open your project or create an empty project.
  3. Select the Assets tab.
  4. Select New asset > Solve optimization problems in the Work with models section.
  5. Click Local file in the Solve optimization problems window that opens.
  6. Browse to choose the StaffPlanning.zip file in the Model_Builder folder. Select the relevant product and version subfolder.
  7. If you haven't already associated a watsonx.ai Runtime service with your project, you must first select Add a Machine Learning service to select or create one before you choose a deployment space for your experiment.
  8. Click Create.
    A Decision Optimization model is created with the same name as the sample.
  9. Working in Scenario 1 of the StaffPlanning model, you can see that the solution contains tables to identify which resources work which days to meet expected demand.
    If there is no solution displayed, or to rerun the model, click Build model in the sidebar, then click Run to solve the model.

Using a random generator to create new scenarios

Procedure

To create new scenarios using a randomized data using a sample:

  1. Select the Assets tab.
  2. Select New asset > Work with data and models in Python or R notebooks in the Work with models section.
  3. Select the Local file tab in the new window that opens.
  4. Click Drag and drop files or upload and browse to choose the CopyAndSolveScenarios notebook from the jupyter folder. Select the relevant product and version subfolder.
  5. Click Create Notebook.
    The notebook opens in your project.
  6. In the Settings tab of your project, locate the Access Tokens section, and click New token +. Enter a token name, select Editor as the Access role and click Create.
  7. Return to your notebook from the Assets tab of your project and click the pencil icon to edit it. In the More menu More menu icon containing three vertical dots, select Insert Project Token. This adds your authorization token in a hidden cell.
  8. From the main home Navigation Menu, select Administration > Access (IAM) > API keys. Create and copy your API key.
  9. Return to your CopyAndSolveScenarios notebook and locate the cell containing client=Client(pc=pc,apikey="API_key", and replace API_key with your own IBM Cloud API key that you just copied.
  10. Locate the cell containing decision = client.get_experiment(name="StaffPlanning").
    This cell instructs the notebook to copy Scenario 1 from the StaffPlanning model and use it to generate additional scenarios based on randomized data. If you’ve used another name for your model, replace Staffplanning with the name you chose.
  11. Run the notebook using Cell>Run All.
    The notebook uses the Python random module to generate data for five additional scenarios in the model named StaffPlanning. The new scenarios are named Copy 01 ... Copy 05. The number of scenarios to generate is specified in cell 9, N_SCENARIOS = 5.
  12. Open the StaffPlanning model to compare the solutions of the different scenarios. Click the Scenarios icon to open the Scenario pane and quickly move between scenarios. You can also see all your scenarios at a glance in the Overview.
  13. Click Visualization in the navigation pane to compare the different scenarios on the Multi Scenario tab.
    The Demand chart plots the demand for the different periods in the randomly generated scenarios. The KPIs chart plots the total cost across the randomly generated scenarios. The My KPIs chart provides a heat map of costs for the different scenarios along with the mix of temporary and fixed resources for each.

Results

This example shows how easily you can test your model by generating additional scenarios based upon randomized data. Such testing makes it possible to assess whether the model is robust enough to perform effectively in an environment with variable data.