Optimization

Optimization platform process diagram

This diagram shows the general Optimization modeling process:

• Data Collection
  • Datasets: Import your data and save it in an accessible format for further processing.
  • Groups: Collect multiple datasets in a single group so as to fed these multiple datasets to the quest in a single go.
• Optimization quests: Select the respective group (multiple datasets), prepare the data, build the optimization problem using setup model, select the configured setup model and apply the respective solver using optimize model activity and run, and then review the results of the model. Move the most optimized problem to production and deploy it to run for real time.
• Endpoints: View and test the deployed models.

Quests

A quest is the AI model that you build and deploy. These approaches are available:

• Fast-track deployment
  • A streamlined user interface designed for efficient workflows, featuring a single Select model activity to configure and deploy custom algorithm models as real-time endpoints.
• Design and deployment
  • In design and deployment, there are several stages involved in process:
    • The quest starts from selecting the group created in data collection step.
    • Pre-processing steps are used to prepare the data by cleaning or transforming it. Single or combination of datasets can be prepared in the optimization platform. Currently, the application supports three datasets to be joined and prepared at a time by applying the respective transformation activities.
    • The quest allows you to create one, or multiple. setup models where sets, indices, constants, decision variables, constraints, and objective functions are defined preparing the mathematical model.
    • There are multiple predefined solvers available in the optimizer that are applied on the prepared mathematical model to solve. The optimizer executes and resolves the problem, generating the most optimal solution.
    • The outcomes are evaluated that are generated under results activity.
    • The best model is taken into production.

Endpoints

The endpoint is deployed from the real-time production quest. The endpoint represents the deployed model that is called to get real time predictions.

Custom algorithms

You can package and deploy custom algorithm code for optimization module in Infor AI.

After the custom algorithm is registered and deployed, you can use it in a quest to expose the model for consumption. The custom algorithms can be packaged and deployed as follows:

• Fast-track CA: This custom algorithm is registered for use in the Fast-track deployment quest and can be exposed as a realtime endpoint.
• Standard CA: This custom algorithm is registered for use in the Design and deployment quest and can be exposed for batch consumption or as a realtime endpoint.

Solver Quick Reference

Use the Solver Quick Reference to decide which solver is the most suitable from the list of available solvers based on problem type. The solvers are mapped to the various problem types that are possible in the Optimization module.