[indiesolver]

Thank you for providing useful references. Hyperparameter tuning can accelarate CPLEX by 10x and more depending on problem instances (however, it it true that each new version of CPLEX is faster and faster).

What I meant is that in the case if your problem is formulated in a way that the CPLEX and Gurobi cannot treat it (e.g., stochastic and multiobjective) and is not very large-scale, then one can use heuristics. However, the efficiency of the latter will likely depend on hyperparameter settings which need to be set properly.

[wenc]

> What I meant is that in the case if your problem is formulated in a way that the CPLEX and Gurobi cannot treat it (e.g., stochastic and multiobjective) and is not very large-scale, then one can use heuristics. However, the efficiency of the latter will likely depend on hyperparameter settings which need to be set properly.

Ah makes sense... stochastic and multiobjective formulations have superstructures that are not exploited by MIP solvers by default, so hyperparameter tuning might be useful. Creating (exact) heuristics for these superstructures are also an active area of research.

Some solvers like CPLEX are starting to natively support higher level structures like Benders decompositions [1], but they will never support every structural variation.

[1] Benders: https://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.7.0...