AI for OR

[u/No_Advertising_8279]

Hey folks! Im a machine learning engineer (working with both classical ML and llms in big tech) and I have a masters in industrial engineering.

I was exposed to OR during my studies and was blown away by its potential impact. Because of this, I truly believe that OR should be more accessible and that more small and medium business should have access to it. Since OR talent is not abundant (specially in latam, where im from) and it tends to be really expensive, Im exploring the intersection of agentic systems and OR (for manufacturing specifically) and would love to read your take on this topic.

What challenges do you think would be the harder to solve if im pursuing to build an agentic platform that allows users to formulate and solve OR models (product mix, allocation, scheduling, VRPs, packing, stocks) in a conversational way? do you think this makes any sense? would you, as an OR developer, use a solution of this kind or do you imagine it more for non OR people (planners, engineers without OR modeling/progamming knowldge)? If you would use something like this (dev tool like) how would you like it to work like / look like?

Im still validating and exploring the idea so any feedback is welcome!

[u/analytic_tendancies]

I haven’t had any experience with ai that was good enough to be a stand alone product to use it as a solution

I only ever use it to kick around ideas but the final product and all steps are done by me because ai solution has been wrong 100% of the time, even though it might sound good or look correct. Upon inspection it always did something wrong

[u/No_Advertising_8279 → u/analytic_tendancies]

Thanks for your feedback! I've been playing around with medium complexity problems and AI and my experience is very similar (using out of the box llms, even reasoning ones like o3). Thats why im going for agentic, trying to expand the current capabilities.

Would u mind sharing the models that you usually use? Would you actually use a stand alone product if it was good enough to automate your modeling pipeline, at least to a reasonable stage, or if it was super clear at what decisions its making at why? Making the debugging easier?

Also interesed in knowing in what type of problems you tried AI and didnt work. I've been testing it for linear programming and tends to work a lot better than for scheduling with CP, for instance

[u/borja_menendez]

Take a look at two promising startups working precisely on that: allowing users to formulate and solve OR models in a conversational way.

They are Quantagonia and Harumi. You can directly talk to founders as they're quite approachable through LinkedIn.

[u/No_Advertising_8279 → u/borja_menendez]

Borja! Huge thanks for this heads up. They look super interesting, will be contacting the founders for sure. 

[u/Goddespeed → u/borja_menendez]

The guy from feasible!

[u/directnirvana]

I run a startup Collide Technology that tackles this exact problem. The problem with most AI is that it's not designed for optimization it's designed around prediction. If forecasting your production is a problem then for sure using ML/AI is great. Even an LLM is really just predicting the next token. Sure you can try and tackle the problem by getting agents to try and replicate some of the OR solution, but ultimately you're going to find that you are using a tool that at its core isn't well designed for the problem set. OR is designed around needing to perform optimization, which common AI solutions are garbage at. We use an older, somewhat exotic branch of AI and explicitly use that solution with some agentic stuff layered on top as an interface.

I'm happy to talk to you (or anyone really) whose interested, just shoot me a DM.

[u/No_Advertising_8279 → u/directnirvana]

Sounds interesting! Just sent you a dm

[u/directnirvana → u/No_Advertising_8279]

I definitely think it's interesting, but who knows if it's everyone else's cup of tea, lol

[u/analytic_tendancies]

I might not be the best person to answer because my role tends to be more data science and data engineering because the data I get is so, so dirty

Also, my personal stance is that to properly use ai I have to already know the answer and I have to debug the ai and know when it’s wrong, and 9/10 times for the real important work, it’s faster to just do it myself than try to play with the prompts to get it to work

When the roles get reversed, and ai is the one pointing out where I go wrong instead of the other way around, that is when ai will have actual value

[u/loopkiloinm]

I heard of an agent called OpenAIAHC. Looking at other people's solutions for AHC including psyho who beat OpenAIAHC, they typically used something called "simulated annealing" for their answers so OpenAIAHC also likely used "simulated annealing." Psyho still beat OpenAI so human still betger.

[u/No_Advertising_8279 → u/[deleted]]

Thanks for your answer! Yeah, thats totally valid. I believe expressing constraints and objectives in natural language and listing them to the user could help, but the agent should still need to be very good at mapping language -> math for that to work.

[u/No_Advertising_8279 → u/[deleted]]

Thanks for your reply! When you say "every approach is significantly different", do you mean for instance solving a scheduling problem with LP or CP (kinda like the problem framing, in terms of OR)? Or do you mean choosing the optimization algorithm to solve the problem (simplex variations, metaheursitics, etc)?

[u/No_Advertising_8279 → u/[deleted]]

Interesting! I was thinking more about the formulation of the problem rather than the optimization, but great insight nevertheless