Optimizing Assortment Selection

1

Navigate

Navigate to the Artificial Intelligence section of the Infor CloudSuite.

Navigate to the 'Datasets' section option under the 'Data Collection' tab on the lefthand menu.

2

Add datasets

Add the datasets included in the download

• Import data from 'BudgetLimit.csv', 'SelectionProblem.csv', and 'SpaceLimit.csv' as three distinct datasets.
• For each imported file, provide the following details and save the dataset:
• Name: A unique identifier referring to the current file's dataset.
• File: The current file to upload and import.
• Delimiter: The character or symbol used to separate individual fields (columns) within the dataset file.

Save after creating each dataset. You don't have to wait for the data to be loaded before moving to create the next dataset.

Note: In a shared training/testing environment, it is recoomended to add your initials to all the variable names to find your content more easily, for example XX_Budget_Limit.

3

Create group

Navigate to the 'Groups' option under the 'Data Collection' tab.

Add a data group, select all three files for the group, and assign a name. We used Profit_Optimization.

4

Create quest

1. Navigate to the ‘Quests’ section. Open the 'Optimization' option in the left-hand menu. The 'Quests' feature is where we will build the model.
2. Click 'Add New Quest' and provide the following details:

Name: A unique identifier for the optimization model. In this case, name the model "Profit_Optimization".

Set Data Collection: Select the data group created earlier. In this case, select 'Profit_Optimization' (or the name you chose).

5

Setting up the model

Drag and drop the 'Setup Model' tile into its designated column.

Provide the naming information: "Profit Optimization".

Fill in the Sets/Indices & Constant section:

• 'Space_Limit' → 's'
• 'Item_Details' → 'i'
• 'Budget_Limit' → 'b'

Click 'Save'.

6

Create decision variables

1. Click on the 'Decision Variables' tab.
2. Click the '+' button to add a new decision variable:
3. Name: "ItemSelected"
4. Variable: "x"
5. Dimension: 1
6. Index: "i" (representing items in the assortment)
7. Domain: "Binary" (Yes/No selection for an item)
8. Click 'Save'.

7

Set up constraints

Move to the Constraints tab. This section represents the logical conditions that the solution to the optimization problem must satisfy. In this case, the constraints are the budget limit and the amount of space that is available to use. So, add these constraints by clicking on the ‘+’ button and provide the following information.

Name: A unique identifier referring to the constraint.

• Budget to represent our maximum budget constraint.
• Space to represent our space availability constraint.

Dimension: Refers to the dimensionality of the constraint.

• The dimension for ‘Budget‘ is 1.
• The dimension for ‘Space‘ is 1

Index: Refers to the set or range of values that the constraint can take.

• The index variable name for ‘Budget‘ is ‘b‘.
• The index variable name for ‘Space‘ is ‘s‘

Condition: An optional field that allows for more complex logical filtering. We will not be defining a condition in this tutorial. For more information, refer to the product’s documentation.

Function Declaration: The mathematical expression that defines the constraint. You can use the expression builder to help build this mathematical expression by providing the appropriate parameters. Click on the pencil to provide the following information and be sure to click the ‘Save’ button when finished. 

You can copy the following functions for Budget and Space constraints, or use the ‘Expression Builder’. When using the ‘Expression Builder’, note that the SUM is called Sigma under the ‘Insert Function’ list. Pay close attention to right order of items in your function. Note! Your naming might differ from the example. 

• For ‘Budget‘, the function declaration is the ‘sigma function‘ for the sets/indices ‘i‘ & ‘b‘ of the decision variable representation ‘x‘ and has these parameters: ‘Item_Details.COST‘ and ‘Budget_Limit.BUDGET_LIMIT‘. This expression translates to the sum of the costs of corresponding selected items in the assortment cannot exceed or must be less than or equal to the allocated budget.
• Budget Constraint: SUM<i>{x[i] * Item_Details.COST[i]} <= Budget_Limit.BUDGET_LIMIT[b]
• For ‘Space‘, the function declaration is the ‘sigma function‘ for the sets/indices ‘i‘ & ‘s‘ of the decision variable representation ‘x‘ and has these parameters: ‘Item_Details.SPACE‘ and ‘Space_Limit.SPACE_LIMIT‘. This expression translates to the sum of the available spaces of corresponding selected items in the assortment cannot exceed or must be less than or equal to the space available.
• Space Constraint: SUM<i>{x[i] * Item_Details.SPACE[i]} <= Space_Limit.SPACE_LIMIT[s]

Click ‘Save’.

8

Set up objectives

Move to the Objectives tab where we set our goal. This section represents a mathematical equation that maximizes or minimizes a numeric value to solve the optimization problem. Click on the ‘+’ button to add a new objective function and provide the following information. Be sure to click the ‘Save’ button when finished.

• Name: A unique identifier referring to the objective, which will be named, ‘Revenue‘.
• Sense: Specifies the direction of the goal, whether to maximize or minimize the value of the objective. In this case, select ‘MAXIMIZE‘ from the drop-down menu.
• Function declaration: The mathematical expression that defines the objective. You can use the expression builder to help build this mathematical expression by providing the appropriate parameters. Click on the pencil to provide the following information and be sure to click the ‘Save’ button when finished. In this case, the function declaration is the ‘sigma function‘ for the sets/indices ‘i‘ of the decision variable representation ‘x‘ and has the parameter ‘Item_Details.POTENTIAL_REVENUE‘. This translates to the sum of each selected item in the assortment should reach its maximum potential.
• Objective function: SUM<i>{x[i]*Item_Details.POTENTIAL_REVENUE[i]}

9

Optimize model

Return to the quest layout, and add an Optimize Model block to the workflow. Provide the following information and be sure to save your work when finished:

Name: A unique identifier referring to the optimize model object, which we will name ‘Optimize CBC‘.

Solver: Refers to the optimization algorithm or solver that will be used to find the optimal solution for the model. In this case, we will stick with the default, ‘CBC‘ (Coin-Or Branch and Cut), which is an open-source linear program (LP) and mixed-integer program (MIP) solver. This solver is intended to be used primarily as a callable library to create customized branch-and-cut solvers. Refer to the ‘Solvers Reference‘ section under ‘Optimization‘ for more information about the different solvers.

Model/Custom Algorithm: Refers to the optimization model that we are currently using, which will be ‘Profit Optimization‘ in this tutorial.

Iterations: An optional field that refers to the limit the number of iterations that will be used. This is helpful to use for large or time-consuming tasks. Endpoint Schema: Refers to the all the components that are going to be included in the JSON message.

• Parameters Selected: Select all the parameters from the selection list on the left.
• Constants Selected: Select all the constants from the selection list on the left.
• When finished with selections, click on the ‘Run’ button and ‘Save’ button within the ‘Endpoint Schema’ window.

10

Compare solvers (optional)

Drag and drop another ‘Optimize Model‘ tile as we utilize another solver, so that we can make comparisons. Be sure to save your work when finished.

Name: ‘Optimize Bonmin‘

Solver: Select ‘BONMIN‘ from the drop-down menu. BONMIN is an open-source C++ code for solving linear (LP), non-linear, and general mixed integer nonlinear programming (MINLP) problems.

Model/Custom Algorithm: ‘Profit Optimization‘.

Endpoint Schema: Refers to the all the components that are going to be included in the JSON message.

• Parameters Selected: Select all the parameters from the selection list on the left.
• Constants Selected: Select all the constants from the selection list on the left.
• When finished with selections, click on the ‘Run’ button and ‘Save’ button within the ‘Endpoint Schema’ window.

11

Run Quest

Save your current progress and run the quest by clicking on the play button.

The ‘Results‘ tile will provide insights about the model’s performance when the optimizer run is complete. We can see the max Revenue is 5128, and that it took the CBC solver only 61 iterations to the get to the solution, while Bonmin needed 410 iterations.

12

Take the Quest to production

We can then deploy the best performing solver, which is the ‘CBC‘ solver in our case as it had less iterations, a faster runtime, and was able to identify the same maximum profit than ‘BONMIN’. Select the solver of choice and click on the ‘Production Quest’ button.  

When prompted to select the type of production quest to deploy, select ‘Realtime Production‘ and click on the ‘Create Production Quest’ button. 

13

Next step

An Endpoint is an exposed access point to a deployed model. The Endpoint has a defined input structure and details what can be expected for output. When it is sent data, the optimization logic runs in the background and reports back the optimal result 

Once the CBC model has been put to Production, go to the ‘Realtime Production’ tab and then to the ‘Optimize Model’ step.  

Click on Endpoint Schema. Choose all variables/constants and click Run on Runtime Input Schema.  

SAVE and then click ‘Deploy Endpoint’. The Quest is now ready to be run with data. Once you run the Production model. Your Endpoint will be visible in the ‘Optimization/Endpoints’ where you can select an endpoint from the list to activate, deactivate, or delete. You must deactivate an endpoint before you can delete it. Additionally, you can select an endpoint and update the description.