Finding the optimal solution to a linear programming model is important, but it is not the only information available. There is a tremendous amount of sensitivity information, or information about what happens when data values are changed.
Recall that in order to formulate a problem as a linear program, we had to invoke a certainty assumption: we had to know what value the data took on, and we made decisions based on that data. Often this assumption is somewhat dubious: the data might be unknown, or guessed at, or otherwise inaccurate. How can we determine the effect on the optimal decisions if the values change? Clearly some numbers in the data are more important than others. Can we find the ``important'' numbers? Can we determine the effect of misestimation?
Linear programming offers extensive capability for addressing these questions. We begin by showing how data changes show up in the optimal table. We then give two examples of how to interpret Solver's extensive output.