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.

Strong branching consumes significantly more computation time per node than the default variable selection strategy.

To activate strong branching :

• In the Interactive Optimizer, use the command set mip strategy variableselect 3.

• In the Concert Technology Library, set the integer parameter VarSel.

• In the Callable Library, set the integer parameter CPX_PARAM_VARSEL.

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. Table 5.9 summarizes those limits and shows the parameter names.