Sample models and notebooks for Decision Optimization

There are several examples presented in this documentation as tutorials and there are many samples provided for IBM Watson Studio.

The Decision Optimization GitHub contains a repository of samples for use with IBM Watson Studio. These samples are to be used in the Decision Optimization model builder. This repository also contains Jupyter notebooks which can be imported into Watson Studio.

The following table shows examples provided in this documentation, which show you how to use Decision Optimization for Watson Studio.
Table 1. Decision Optimization documentation examples

Learn how to ...


Create scheduling models using the Modeling Assistant

House Construction example

  • Create, edit and solve a planning and scheduling model with the Modeling Assistant
  • Create and examine different scenarios

Solving a model using the Modeling Assistant

Create Python optimization models using the Decision Optimization model builder

Diet example

  • Create and solve a Python model generated from an existing scenario
  • Create and examine a new scenario

Solving a Python DOcplex model

Multiple scenarios example

  • Create a Python model from a Python notebook imported into Decision Optimization and solve it
  • Generate multiple scenarios from a Python notebook using randomized data

Working with multiple scenarios

Create or import DOcplex Python notebooks

Decision Optimization notebook examples

  • Download a notebook and add it to a project
  • Run a notebook

Running Decision Optimization notebooks

Decision Optimization model builder samples (Modeling Assistant, Python, OPL)

The following table lists the Decision Optimization samples that are provided in DO-samples in the Decision Optimization GitHub. All these assets use the model builder and contain data.


To run models you must associate a Watson Machine Learning instance with your Watson Studio project and associate a deployment space with your Decision Optimization experiment.

To use these samples:
  1. Download and extract all the DO-samples on to your computer. You can also download just the one sample, but in this case do not extract it.
  2. Create a project in IBM Watson Studio. Select Create an empty project, enter a project name and click Create.
  3. In the Overview tab of your project, click add a Machine Learning service and select an existing service instance (or create a new one) and click Select.
  4. Click Add to Project.
  5. Select Decision Optimization experiment.
  6. Select the From file tab in the Decision Optimization experiment pane that opens.
  7. Click Add file. Then browse to the Model_Builder folder in your downloaded DO-samples. Select the relevant product and version subfolder. Choose your sample .zip file.
  8. Choose a deployment space from the drop-down menu (or create one) and click Create. If you haven't already associated a Machine Learning service with your project, you must first select Add a service to select or create one, before choosing your deployment space for your experiment.
  9. Click Create.
A Decision Optimization model is created with the same name as the sample.
Table 2. Decision Optimization Models
Models for Decision Optimization Problem type Model type
Diet Blending Python
StaffPlanning Multi-Scenario Planning Python
BridgeScheduling Scheduling Modeling Assistant
HouseConstructionScheduling Scheduling with assignment Modeling Assistant
MarketingCampaignAssignment Resource Assignment (Scenarios 1 - 4)

Selection and Allocation (Scenario 4 - Selection)

Modeling Assistant
SupplyDemandPlanning Supply & Demand Planning Modeling Assistant
PortfolioAllocation Selection & Allocation Modeling Assistant
PastaProduction Production OPL
Multifiles Using a model with multiple files. Python and LP

Jupyter notebook samples

There are also Jupyter notebooks provided in the Decision Optimization GitHub that do not use the model builder. To use these Python notebook samples :
  1. Download and extract all the DO-samples on to your computer. You can also download just one sample.
  2. Create a project in IBM Watson Studio.
  3. Click Add to Project.
  4. Select Notebook as your asset type.
  5. Select the From file tab in the New Notebook pane that opens.
  6. Name your notebook and browse to select the notebook from the jupyter folder selecting the relevant product and version subfolder in your downloaded DO-samples.
  7. Click Create Notebook. The notebook is added to your project.

Python notebooks in the Watson Studio Gallery

Decision Optimization Python notebooks are available from the Watson Studio Gallery. To use these in an existing project, open a notebook in the Gallery, click the Add to project button, select your Project and click Create Notebook.