📈 The programming language of OR, which one are you using, OR-Tools wins again

Local Optimum: short, imperfect-yet-useful ideas - Edition #22

Welcome to a new edition of Local Optimum: a short, imperfect-yet-useful collection of ideas related to optimization, decision-making, and applied Operations Research.

Let’s dive in! 🪂

1) 👑 The programming language of OR

Machine learning catapulted Python into default status.

There’s a huge ecosystem of libraries that make prototyping models and deploy production code effortless. When ML teams standardized on Python, the rest of the analytics world took notice.

Operations Research followed naturally. OR projects increasingly need data-driven pipelines and seamless integration with company data stacks. Python offers all of that in one place: mature OR toolkits, direct access to ML frameworks, and production-ready web frameworks for APIs.

The result is a faster prototyping, easier collaboration with data scientists, and a single language from data ingestion to optimization and deployment.

Python is the bridge between classical optimization and the ML workflows shaping modern decision-making.

2) 🙊 Which one are you using?

There’s one clear winner, but I’d love to hear your thoughts on this:

(click on the image to go to the post)

3) 🥇 OR-Tools wins again

Last Monday, the MiniZinc challenge announced its winners in different categories.

And without any surprise, the CP-SAT solver from Google OR-Tools won again:

It’s a powerful solver.

I’ve been using it for more than 3 years now, and I had a couple of things to say about it, especially if you’re looking for good documentation.

You can read the post here:

📈 #76 Optimizing with Google OR-Tools: strategies and insights from three years of real-world experience

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Are you the owner of your results?

That’s the same as asking if you’re the owner of the data that feeds your models/algorithms.

Because it’s common that users look at the plan and say “yes, this works” or they dismiss it outright.

And often, the difference between trust and rejection doesn’t lie in the math. It lies in the data.

Next Monday, I’ll share my thoughts on data ownership as an OR Engineer.

I’ll cover:

🔑 Why you must own the inputs
🧭 What ownership really means
🛠️ How to put it into practice

If you are using real-life data to feed your models, this will be useful. See you Monday!

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And that’s it for today!

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Have a nice day ahead ☀️

Borja.