Sensitivity Analysis
SENSITIVITY ANALYSIS
The solution obtained by simplex or graphical method of LP is based on deterministic assumptions i.e. we assume complete certainty in the data and the relationships of a problem namely prices are fixed, resources known, time needed to produce a unit exactly etc. However in the real world, conditions are seldom static i.e. they are dynamic. How can such discrepancy be handled? For example if a firm realizes that profit per unit is not Rs 5 as estimated but instead closer to Rs 5.5, how will the final solution mix and total profit change? If additional resources, such as 10 labor hours or 3 hours of machine time, should become available, will this change the problem's answer? Such analyses are used to examine the effects of changes in these three areas:
1. Contribution rates for each variable C FACTOR
2. Technological coefficients A FACTOR
3. Available Resources B FACTOR
This task is alternatively called sensitivity analysis. It is also called as post optimality analysis.
Sensitivity analysis often involves a series of what if? questions. What if the profit of product 1 increases by 10%? What if less money is available in advertising budget constraints? What if new technology will allow a product to be wired in one-third the time it used to take? So we can see that sensitivity analysis can be used to deal not only with errors in estimating input parameters to the LP model but also with management's experiments with possible future changes in the firm that may affect profits.
There are two approaches to determining how sensitive an optimal solution is to changes. The first is simply a trial and error approach, however we prefer the second approach of post optimality method i.e. after an LP problem has been solved, and we attempt to determine a range of changes in problem parameters that will not affect the optimal solution or change the variables in the solution. This is done without resolving the whole problem again. This
The solution obtained by simplex or graphical method of LP is based on deterministic assumptions i.e. we assume complete certainty in the data and the relationships of a problem namely prices are fixed, resources known, time needed to produce a unit exactly etc. However in the real world, conditions are seldom static i.e. they are dynamic. How can such discrepancy be handled? For example if a firm realizes that profit per unit is not Rs 5 as estimated but instead closer to Rs 5.5, how will the final solution mix and total profit change? If additional resources, such as 10 labor hours or 3 hours of machine time, should become available, will this change the problem's answer? Such analyses are used to examine the effects of changes in these three areas:
1. Contribution rates for each variable C FACTOR
2. Technological coefficients A FACTOR
3. Available Resources B FACTOR
This task is alternatively called sensitivity analysis. It is also called as post optimality analysis.
Sensitivity analysis often involves a series of what if? questions. What if the profit of product 1 increases by 10%? What if less money is available in advertising budget constraints? What if new technology will allow a product to be wired in one-third the time it used to take? So we can see that sensitivity analysis can be used to deal not only with errors in estimating input parameters to the LP model but also with management's experiments with possible future changes in the firm that may affect profits.
There are two approaches to determining how sensitive an optimal solution is to changes. The first is simply a trial and error approach, however we prefer the second approach of post optimality method i.e. after an LP problem has been solved, and we attempt to determine a range of changes in problem parameters that will not affect the optimal solution or change the variables in the solution. This is done without resolving the whole problem again. This