Hybridization of ML and OR

The future of ML (and OR!) lies in the hybridization of ML and OR.

One of our main mottos is:

Where machine learning meets operations research

and this truly is where Funartech shines and distinguishes itself from other companies. We strongly advocate for the combination of ML and OR for all industrial projects.

OR and ML might not be sufficient but they should be considered in any analytical solution.

There are basically 4 ways to combine ML and OR:

  1. The now "classical" use of ML as a pre-process and OR as a post-process steps. Read more about this approach .
    The next two approaches are purely technical and only of importance to ML and OR practitioners.
  2. Use OR to improve ML. Read more about this approach .
  3. Use ML to improve OR. Read more about this approach .
  4. A complete hybridization of both ML and OR where we develop new algorithms. Read more about this approach .

ML and OR as separate blackboxes

This is the most common approach whenever companies propose to combine ML and OR. Often, ML is used to predict and OR is then used to optimize on those predictions.

As an example, let's say you are a train company and you want to repair/replace your tracks. Of course, you'll want to do this for the minimum cost. How can you do this? Decouple this problem in two steps:

  1. Use ML to detect your defects
    Post cameras under the wagons of your trains, take pictures in rapid succession. With computer vision and deep learning, find the defects on the pictures and stitch your pictures together to obtain a network/graph of defects of your tracks. You can classify the nature of defects into several classes:
    • to be replaced:
      • immediately
      • in 3 months
      • in 6 months
      • ...
    • to be repaired:
      • immediately
      • in 3 months
      • in 6 months
      • ...
  2. Use OR to optimize your repair/replacement policy
    Once you have your network/graph of defects, you still need to know how to replace/repair your tracks in an optimal way. If a maintenance team is on site to repair a track, it might be less costly for it to repair another close-by track that doesn't need immediate repair. OR will choose an optimal repair/replace policy for your tracks.

You might want to have a look at our use cases as they all use that approach.

Finally, the first combination of ML and OR becomes mainstream in 2019!

We can now say that combining ML and OR is becoming mainstream...

Use OR to improve ML

OR can be used to optimize, i.e. minimize or maximize some function in ML. But wait, isn't this what ML is about? Exactly, ML is strongly based on OR optimization when it optimizes its predictions.

Use ML to improve OR

It also goes the opposite way. The strong point of ML is to predict. Some research is conducted in order to predict on what variable and how to branch in search trees.

A complete hybridization of both ML and OR

We are developing new algorithms that are a complete and total hybridization of both ML and OR. These new algorithms don't treat ML and/or OR as separate black boxes but are really a new combination of the strengths of both fields. We are convinced that these new algorithms are the way to go to solve any problem and that in the future OR and ML will be merged into a new field.

We use this approach for our Open door to AGI project.