For some models, the Best Node value in the node log changes very slowly or not at all. Runtimes for such models can sometimes be reduced by the variable selection strategy known as strong branching. Strong branching explores a set of candidate branching-variables in-depth, performing a limited number of simplex iterations to estimate the effect of branching up or down on each.

To activate strong branching, set the VarSel parameter to a value of 3.

On rare occasions, it can be helpful to modify strong branching limits. If you modify the limit on the size of the candidate list, then strong branching will explore a larger (or smaller) set of candidates. If you modify the limit on strong branching iteration, then strong branching will perform more (or fewer) simplex iterations per candidate.

These limits are controlled by the parameters StrongCandLim and StrongItLim, respectively.

Other parameters to consider trying, in the case of slow movement of the Best Node value, are non-default levels for Probe (try the aggressive setting of 3 first, and then reduce it if the probing step itself takes excessive time for your purposes), and MIPEmphasis set to a value of 3.