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I helped implement production and labor planning software on top of FICO xpress some years ago. The paradigm of LP/ILP was all new to me though I was very much into math. Our software was solving hierarchical/nested optimization problems involving millions of variables and constraints every 15 minutes. It felt like magic that this was possible. I would really encourage anyone that has never worked with these tools before to explore them as it can open a palette of tools and ideas you may not have thought of before. A great free way to get into this is to read the examples and use PuLP for Python.


The best solvers are really impressive. Unfortunately, they also cost a very large sum of money.

We should be happy that many of the best tools in other areas are free.


Pretty cool you got to see this stuff in the real world!

Questions:

1) Was the problem able to be solved with one machine?

2) Was the problem reliably able to be solved within a time-bound, or was it "spiky"?

3) Was the solution all or nothing? Aka would you get a partial/suboptimal solution after a time-bound?


Not an author but

1) It is usually done on a single machine. Often times even on single core.

2) Spikiness of the solve time is a real problem in practice.

3) You get partial solutions but they tend to be fare apart but with great improvements.


Thank you, that's useful!




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