Conversation
…r advanced farmers
…to check correctness
natasha-patnaik-321
changed the title
rachaelalfant
left a comment
rachaelalfant
left a comment
Collaborator
There was a problem hiding this comment.
Have we fixed the highspy issue with the different versions of Python? It looks like the tests are passing, but just wanted to double check!!
rachaelalfant
approved these changes
Aug 13, 2026
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Two versions of the farmer problem, used as an exemplar for the MRP algorithm in the single-fidelity setting.
Basic farmers = Classic 3-scenario problem from Birge & Louveaux
Advanced farmers = For each crop, do a linear interpolation between the BelowAverage scenario yield and the AboveAverage scenario yield from the Basic farmers problem. For each crop, select any one of these yields with equal probability. The full scenario distribution is a discrete uniform over all resulting scenario vectors.
For the ACV-MRP algorithm in the multifidelity setting: see mrp_discrete_facilityloc.
NOTE: a more illustrative multifidelity example may be considered in the future, but for now, leaving this as the main example to demonstrate usage & capabilities.