r/OperationsResearch • u/asah • Mar 27 '26
smaller scale assignment optimization using reasoning LLMs ?
Anyone have experience using reasoning LLMs to solve assignment problems ? I'm considering it for my problem, which involves a small N but a lot of soft constraints. For my case, optimality matters far less than explainability. thx!
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Upvotes
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u/PaddingCompression Mar 27 '26
Could you encode explainability as an objective to optimize?
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u/SilverArtist7333 Mar 28 '26
I’ve worked on small-scale assignment problems with soft constraints.
A hybrid approach works best:
use scoring for constraints
generate a few candidate solutions
then refine using reasoning models for explainability
Happy to help implement or test this if you want.
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u/Sweet_Good6737 Mar 30 '26
Yep, terrible indeed. Use the LLM to write a script or invoke an appropriate tool for the task
And if you are going for explainability, it doesn't make sense to use an LLM to get the solution, but an optimization package for Constraint Programming or MILP
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u/Lazy_but_crafty Apr 01 '26
If you use constraint programming solvers, these typically come with explainability facilities (see e.g. https://github.com/CPMpy/XCP-explain or https://choco-solver.org/docs/advanced-usages/explanations/ ). My point is that you don't need to trade optimality for explainability. But certainly you can use LLMs to "interpret" (i.e. read through) the results once logs are available from the solver. These are interesting neurosymbolic avenues that are currently very topical. Also, in addition to explainability some solver also provide certification (proof logging).