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C H A P T E R

Linear Programming Sensitivity Analysis

4

SOLUTIONS TO DISCUSSION QUESTIONS AND PROBLEMS
4-1. In most real world situations that are modeled using LP, conditions are dynamic and changing. Hence, input data such as resource availabilities, prices, and costs used in the LP model are estimated, rather than known with certainty. In such environments, sensitivity analysis can be used to identify the ranges of values of these input data for which the current LP solution remains optimal. This is done without solving the problem again each time we need to examine a change in an input data’s value. When all model values are deterministic, that is, known with certainty, sensitivity analysis may not be needed from the perspective of evaluating data accuracy. This may be the case in a portfolio selection model in which we select from among a series of bonds whose returns and cash-in values are set for long periods of time. 4-2. Sensitivity analysis is important in all decision modeling techniques. For example, it is important in breakeven analysis to test the model’s sensitivity to selling price, fixed cost, and variable cost. Likewise, it is important in inventory models in which we tests the result’s sensitivity to changes in demand, lead time, costs, and so on. 4-3. A change in a resource’s availability (right-hand-side) changes the size of a feasible region. An increase means more units of that resource are available, causing the feasible region to increase in size. A decrease means fewer units are available. Obviously, if more units of a binding resource are available, it may be possible for the optimal objective value to improve. In contrast, if more units of a non-binding resource are available, the additional units would just contribute to more slack and there would be no improvement in the optimal