Linear optimization: Sensibility analysis 3: Unterschied zwischen den Versionen

Version vom 27. Juni 2013, 23:24 Uhr

Theory

The sensitivity analysis is concerned with the effects and changes in the output data to the optimal solution. You can also ask this question a little differently: to what extent can size change without affecting the essential properties of the solution?

Finding the optimal solution to a linear programming model is important, but one can derive the Sensitivity Analysis important additional information from the linear model (but there are much more information that can be read from an analysis). There is a tremendous amount of sensitivity information, or information about what happens when data values are changed.

Other analysises you could only use under the condition that a constancy of the output data is given. However, such a stability of the output data does not exist in reality. That is a problem because you usually cannot modify any data simultaneously, only one size while keeping all other constant ("ceteris paribus") is changed and asked: In what area can the size in question vary, without this, the solution loses its validity?

By "valid" here are the qualitative characteristics of a solution and not understood quantitatively. A solution is qualitatively different (structural) only by another if at least one pivot operation is needed to establish admissibility and / or optimality again if it was lost in the change.

Non basis variables

Basis variables

Example

Presentation of the problem

Detailed solution process with explanation

Sources

1. Script Operation Research SS 2013 Prof. Dr. Oliver Wendt

2. http://mat.gsia.cmu.edu/classes/QUANT/NOTES/chap8.pdf