Multi-objective environments

When solving a multi-objective model, the solution process typically proceeds in phases, where each phase solves for one objective. The standard algorithmic parameters influence the strategy used to solve the overall multi-objective model. However, in some cases you may want finer-grain control over the strategies used in each phase. The solver enables this through multi-objective environments.

Multi-objective environments are created via API routines (in C, C++, Java, .NET, or Python). You set parameters on these environments as you would with any other environment, but in this case they only affect one of the several objective solves.

To give a simple example, you could do the following:

CC++.NETJavaPython

/* Create multi-objective environments */
GRBenv *env0 = GRBgetmultiobjenv(model, 0);
GRBenv *env1 = GRBgetmultiobjenv(model, 1);

/* Set parameters on multi-objective environments
(for ease of readability, errors are not checked here) */
error = GRBsetintparam(env0, "Method", 2);
error = GRBsetintparam(env1, "Method", 1);
error = GRBsetintparam(env1, "Presolve", 0);

/* Perform multi-objective optimization */
error = GRBoptimize(model);

This would use the barrier solver (Method=2) for the first objective, and the dual simplex solver (Method=1) with no presolve (Presolve=0) for the second. Note that you don’t need a multi-objective environment for each objective - only for those where you want parameters to take different values from those of the model itself.