141–150 of 158 posts
Earlier quoted context omitted.
would you be able to offer some examples and some discussion of how to choose?
https://link.springer.com/book/10.1007/978-1-4419-1665-5
There is a third edition, 9 years later: https://link.springer.com/book/10.1007/978-3-319-91086-4
At Zoba we use CLP, CBC, NLOpt, and OR-tools. Used to use Gurobi. * CLP/CBC: open source makes deployment and devops easy, which is great. Linear models are nice in that you "know what you're getting." Performance is at times a pain point. * Gurobi: super fast, but the licensing was just impossible. Partly that was due to high cost, but ultimately we could have borne the cost; the inability to do something like have…
I noticed that your comment contains no complaint about Gurobi's performance. For comparable problems, was Gurobi superior in solution convergence speed or the quality of the solution itself? Gurobi, AFAIK, is considered the leading edge of the field, and other tools, especially open-source ones, do not perform as well. I cannot speak specifically for your firm and use cases, but in my experience, if the use cases ar…
I would love to use one or more but the process to convert business logic to solver is painful so I ended up having to write a simulated annealing algo in Rust instead. I tried solver.com, Google OR-Tools, and a few other utilities. It was much easier to build a score-calculator for min/max based on user-tweaked parameters, then, jiggle the data, re-calculate score, and keep doing it until there was significant impro…
> so I ended up having to write a simulated annealing algo I think there are much better algorithms in metaheuristic search than just simulated annealing.
I've used custom made implementation in Java ( proprietary based on the owner thesis ) to optimize train crossings for big mining companies like Vale, BHP and Rio tinto, it was stressful but super fun work! Lot's of money to be made on it too if you guys are interested, but it's super niche and hard to get into. There is a huge resistence from train controllers and other workers. I actually understand it because of t…
That's awesome. A comment on a recent thread about a train IT failure went on a bit of an interesting tangent about ahead-of-time network scheduling in (IIUC) the Netherlands' TURNI system - https://news.ycombinator.com/item?id=30902585 (The whole thread was a bit of a wide-spectrum ramble, as one might expect for a downtime event.) So you're saying you were actually doing JIT routing as opposed to AOT? The linked sy…
All the rest was on us, failures, breakages, crashes (We never had one!!!), blocks were super interesting btw!.
So I got on it by chance, I just did interviews as a sport when I was in my early 30's so at some point I passed on this position, pay was awesome, lot's of travel involved, etc... So I joined and learned it all there, it was a small mom and genious software house that specialized on all types of JIT planning for railroads.
JIT was actually "THE" solution to it, because the controllers could just try it during the day, so let's say they new 18:00 was the worst and there was a huge trains comming in at that time with priority, he would just throw shit at the system to see how to same the most time sometimes, of course he knew how to do it by heart, but was it the "Best way" we could help him answering it super fast.
P.S: I am not ducth but I lived there for a while, I remember thinking about how much of a bad day it would have been if it was me, I knew exactly what the NS people were felling :D
You're probably looking for a performance comparison. Look here: http://plato.asu.edu/bench.html This one stays pretty well updated. On the MIP side: * COPT * SCIP * HiGHS seem to be top performers (of the OSS variety). IIRC OR-Tools often by default uses SCIP for its MILP backend.
At Zoba we use CLP, CBC, NLOpt, and OR-tools. Used to use Gurobi. * CLP/CBC: open source makes deployment and devops easy, which is great. Linear models are nice in that you "know what you're getting." Performance is at times a pain point. * Gurobi: super fast, but the licensing was just impossible. Partly that was due to high cost, but ultimately we could have borne the cost; the inability to do something like have…
I loved the JuMP package in Julia for being able to write models once, then swap in different solvers. Most open-source solvers don't handle parallelization well, and they lack the latest research on techniques like branch-cutting and heuristics that can speed things up significantly. In my experience, Gurobi is still leader for linear and MiP solving. But, it's really expensive and the licensing terms seem anachroni…
For MIP and LP I have used CPLEX, Gurobi and to a lesser extent Cbc. I used those three using JuMP (Julia package for mathematical programming) and Gurobi via pulp and pyomo. Of all three, I think Gurobi has a very accessible documentation, note that I am not saying better or more complete which in that case it would go to CPLEX, and the integration with Python straight out of the box is very useful. Cbc is a lifesav…
CPLEX, XPRESS and Gurobi are the gold standard for solving an MIP of a meaningful scale (relative performance will depend on the specific problem but you cannot go wrong with either of these three). Unfortunately there is a big gap between performance of open source v/s commercial solvers in OR space. Type of problems I need to solve are usually unsolvable on open source solvers (very large scale supply chain problem…