Other post-solution information does not have the same meaning in a mixed integer program as in a linear program because of the special nature of the integer variables in the MIP. The reduced costs, dual values, and sensitivity ranges give you information about the effect of making small changes in problem data so long as feasibility is maintained. Integer variables, however, lose feasibility if a small change is made in their value, so this post-solution information cannot be used to evaluate changes in problem data in the usual way of LPs.
Integer variables typically represent major structural decisions in a model, and often many continuous variables of the model are related to these major decisions. With that observation in mind, if you take the integer variable values as given, then you can use post-solution information applying only to the continuous variables in the usual way.
To access this limited sensitivity information in a MIP:
ILOG CPLEX then sets the variable bounds so that upper and lower bounds are those in the current integer solution. You can then optimize the resulting linear program and display its post-solution statistics.
