I worked with MIPs for 8 years and commercial solvers have always been several orders of magnitude faster/better than open-source solvers.
This is generally true for domain-specific software -- unlike general purpose software, incenting a small pool of specialized talent to make open-source contributions is always hard.
The key to high performance in MIPs comes from having good heuristics, not necessarily from improving the basic algorithms (the algorithms are pretty standard -- simplex or interior point). Finding effective heuristics is hard and tedious, but they make a significant difference in solution speed. For instance, naive Simplex may take 40 minutes to solve a problem but with heuristics the solution time might be 5 seconds.
That said, Cbc is competitive for smaller problems, and here's the thing: many production sized problems aren't that big -- it really depends on your problem domain. I've deployed commercial solvers on Cbc (30k variables/constraints) and it was more than adequate.
I don't have any details on this, but Gurobi (a best of class solver) also offers an on-demand cloud SaaS which you can pay for on demand [1]. The economics of this may work out for some types of problems.
Also, if you're in academia, you can get Gurobi/CPLEX licenses for free (yes). My research group in grad school didn't spend a cent on these solvers, and we still got a taste of best-of-class solution performance (that's how they get you :).
I'm not OP, but work with MIP solvers. Yes, there is a massive difference between the commercial (CPLEX, GUROBI, and maybe XPRESS) versus the best open source solvers (CBC/CLP and GLPK). My industry can only use the open source solvers for prototyping small models and could never use one for production with the size models we have. The commercial solvers are extremely expensive too.
This is generally true for domain-specific software -- unlike general purpose software, incenting a small pool of specialized talent to make open-source contributions is always hard.
The key to high performance in MIPs comes from having good heuristics, not necessarily from improving the basic algorithms (the algorithms are pretty standard -- simplex or interior point). Finding effective heuristics is hard and tedious, but they make a significant difference in solution speed. For instance, naive Simplex may take 40 minutes to solve a problem but with heuristics the solution time might be 5 seconds.
That said, Cbc is competitive for smaller problems, and here's the thing: many production sized problems aren't that big -- it really depends on your problem domain. I've deployed commercial solvers on Cbc (30k variables/constraints) and it was more than adequate.
I don't have any details on this, but Gurobi (a best of class solver) also offers an on-demand cloud SaaS which you can pay for on demand [1]. The economics of this may work out for some types of problems.
[1] https://www.gurobi.com/pdfs/user-events/2017-frankfurt/Gurob...
Also, if you're in academia, you can get Gurobi/CPLEX licenses for free (yes). My research group in grad school didn't spend a cent on these solvers, and we still got a taste of best-of-class solution performance (that's how they get you :).